Journal of Educational Evaluation for Health Professions

Research articles

Development and psychometric validation of the Thai AI Literacy Scale for Nursing Students in Thailand: a cross-sectional observational study: Wannaporn Jongchidklang, Supalak Phonphithak, Porntep Amornritvanich; J Educ Eval Health Prof. 2026;23:22. Published online August 4, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.22 [Epub ahead of print]

245 View
34 Download

Abstract PDF: Purpose
This study aimed to develop and validate the Thai AI Literacy Scale for Nursing Students (TAILS-NS).
Methods
A cross-sectional study was conducted across multiple nursing institutions in Thailand from March to May 2026 to address the absence of a validated artificial intelligence (AI) literacy instrument for nursing students in Thai or Southeast Asian contexts. A total of 410 nursing students participated, yielding a response rate of 94.5%. The TAILS-NS was developed through item generation, expert content-validity assessment using the item-objective congruence index, and a 2-phase pilot study, resulting in a 40-item instrument comprising a 15-item knowledge test and 25 Likert-scale items across 6 domains. Exploratory factor analysis using maximum likelihood estimation and confirmatory factor analysis using the weighted least squares mean and variance-adjusted estimator, as well as internal consistency, convergent validity, and discriminant validity, were assessed. An independent validation sample (n=157) was recruited for cross-validation confirmatory factor analysis. Raw data are available as a supplement.
Results
Exploratory factor analysis supported a 6-factor structure: AI awareness, skills, ethics and professionalism, positive attitude, AI anxiety, and readiness. Confirmatory factor analysis showed acceptable model fit, although the root mean square error of approximation (RMSEA) indicated marginal fit (comparative fit index [CFI]=0.919, Tucker-Lewis index [TLI]=0.906, RMSEA=0.090). Reliability of the knowledge subscale was acceptable (Kuder-Richardson Formula 20=0.758). Internal consistency was excellent (α=0.810–0.934; total α=0.974; ω=0.989). Average variance extracted exceeded 0.50 for all factors, supporting convergent validity. Most heterotrait-monotrait ratios were below 0.90, supporting discriminant validity. Cross-validation confirmed factorial replicability (CFI=0.917, TLI=0.904).
Conclusion
The TAILS-NS provides evidence of validity and reliability for measuring AI literacy among Thai nursing students and may serve as a standardized tool for curriculum evaluation. Future studies should expand the AI anxiety subscale and explore cross-cultural applicability in Southeast Asian nursing contexts.

From accommodation to inclusion: understanding barriers and facilitators in clinical dental education for Chilean students with hearing disabilities: a qualitative study: Josefa P. Retamal-Martínez; J Educ Eval Health Prof. 2026;23:20. Published online June 29, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.20 [Epub ahead of print]

492 View
78 Download

Abstract PDF: Purpose
This study aimed to describe inclusive mechanisms supporting dental students with hearing disabilities, focusing on facilitators of and barriers to participation in clinical education.
Methods
A qualitative case study was conducted at a Chilean university. Data were collected through semi-structured interviews with 2 students with hearing disabilities, 3 clinical instructors, and 1 Chilean Sign Language interpreter who works at the university and supports dental students with hearing disabilities, as well as through direct observation. Data were analyzed using inductive thematic analysis. Participant validation was used to enhance credibility.
Results
Facilitators were identified across pedagogical, technological, communicative, and relational domains. Pedagogical adaptations included early access to materials, visual resources, and flexible assessments. Technological tools such as real-time transcription supported communication and access to information. Alternative communication strategies included lip reading, written support, visual cues, and Chilean Sign Language. The presence of an interpreter was a key enabling factor. Relational support from faculty, peers, and family members also contributed substantially. Faculty flexibility and willingness to adapt were particularly important. Barriers were identified at structural, institutional, communicative, and attitudinal levels. Clinical environments presented physical and acoustic challenges, including noise, limited space, and complex interactions. Institutional responses were often reactive and involved limited planning. Communication barriers affected interactions with patients and peers, as well as academic literacy. Faculty reported limited training in inclusive education. Attitudinal barriers included misconceptions about disability and limited experience with disability.
Conclusion
Inclusive mechanisms are present but insufficiently systematized. Strengthening institutional planning and structural adaptations is essential to ensuring equitable clinical education.

Review

Measurement properties of instruments assessing the clinical learning environment in health professions education: a COSMIN-based systematic review: Matthias Michael Walter, Alexander P Schurz, Nele Adriaenssens, Evert Zinzen, Slavko Rogan; J Educ Eval Health Prof. 2026;23:14. Published online June 11, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.14 [Epub ahead of print]

578 View
67 Download

Abstract PDF: Purpose
The clinical learning environment (CLE) is a crucial component of health professions education, providing the foundation for developing profession-specific clinical skills. This systematic review aimed to identify evaluated assessment tools for the CLE in health professions education and to report their measurement properties.
Methods
This systematic review was preregistered (IDESR000098), and its protocol was published previously. Eligible studies were peer-reviewed articles in English that developed and validated tools for assessing the CLE among undergraduate health professions students and followed the COSMIN guidelines for systematic reviews of patient-reported outcome measures. Multiple electronic databases, including MEDLINE, the Cochrane Library, ERIC, Education Research Complete, and CINAHL, were searched; studies were independently screened, and data were extracted. Data were synthesized using best-evidence synthesis according to COSMIN guidelines.
Results
Of the 6,236 articles included in title and abstract screening, 55 were eligible for full-text screening. A supplementary search identified 13 additional articles, resulting in 40 included articles. Overall, 28 tools were identified, with 4 tools (PET, PET-Midwifery, DECLEI, and MidSTEP) demonstrating sufficient content validity. Only MidSTEP demonstrated sufficient structural validity.
Conclusion
Only a minority of the included tools provided sufficient evidence of content and structural validity according to the COSMIN criteria. This finding indicates a systemic need for higher standards in monitoring clinical placements and identifies tools that should be re-evaluated and supported by additional research. Limitations include the exclusion of EMBASE and gray literature and the reliance on studies that predominantly used psychometric-first rather than content-validity-first designs.

Research articles

Development and initial validation of a brief 2-item measure of belonging among student physical therapists in the United States: a psychometric validation study: Robyn Redline, Ashanti Jones, Thomas Gus Almonroeder; J Educ Eval Health Prof. 2026;23:11. Published online May 28, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.11

931 View
78 Download

Abstract PDF Supplementary Material: Purpose
The objectives of this study were to develop a 2-item abbreviated version of the Program Sense of Belonging questionnaire (ProSBq) and to evaluate its ability to identify student physical therapists with relatively low valued competence and social acceptance.
Methods
A cross-sectional study was conducted using survey data from 634 students enrolled in physical therapist education programs across the United States. The 10-item ProSBq was used to assess 2 dimensions of belonging: valued competence and social acceptance. Principal component analysis was performed to identify representative items for each subscale, with 1 item selected per subscale. Pearson product-moment correlations were used to examine relationships between the single items and their corresponding subscale scores. Classification performance was evaluated by assessing how accurately the single-item responses classified students reporting a relatively low sense of valued competence and social acceptance, based on their full ProSBq subscale scores. Multiple single-item response thresholds were examined to assess classification accuracy.
Results
The single items demonstrated strong relationships with their corresponding subscale scores (r=0.63–0.80, with part-whole correction). For valued competence, sensitivity increased from 53.6% to 92.9%, whereas specificity decreased from 96.3% to 73.5% when a more inclusive threshold was used. A similar sensitivity-specificity tradeoff was observed for social acceptance. Receiver operating characteristic curve analyses demonstrated excellent discrimination (area under the curve ≥0.90).
Conclusion
Single ProSBq items demonstrated strong relationships with full valued competence and social acceptance subscale scores and acceptable classification performance. The abbreviated 2-item ProSBq may provide a practical and efficient method for identifying students experiencing low valued competence or social acceptance.

Personality type profiles of medical students and their differences by gender, age, and academic level in Korea: a cross-sectional study: Yera Hur, Sanghee Yeo; J Educ Eval Health Prof. 2026;23:7. Published online April 28, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.7

1,464 View
119 Download

Abstract PDF Supplementary Material: Purpose
Understanding the psychological characteristics of contemporary medical students is essential for effective educational design and learner support. This study aimed to identify medical students’ personality types using a geometric personality assessment tool (GEOPIA), determine whether differences exist by gender, age, or academic level, and explore the practical utility of such profiling for supporting educational practices in medical school settings.
Methods
The 40-item Korean Geometric Psychological Assessment (GEOPIA) was administered to 1,173 students across 5 Korean medical schools. GEOPIA classifies individuals into 4 primary types—Round (sociable, relationship-oriented), Triangle (task-oriented, challenging), Box (prudent, stability-seeking), and Curve (creative, sensitive). Frequency analyses and χ² tests were conducted. Of the 1,016 respondents (response rate, 86.61%), 981 were included in the final analysis.
Results
The most common primary type was Round (40.3%), followed by Box (31.7%), Triangle (15.2%), and Curve (12.8%). Across the 12 combined profiles, Round–Box (21.9%) was the most prevalent, followed by Box–Round (19.0%) and Round–Triangle (9.7%). No significant differences were observed by gender (χ²=6.360, P=0.095, Cramer’s V=0.082), age (χ²=11.454, P=0.490, Cramer’s V=0.065), or academic level (χ²=18.044, P=0.260, Cramer’s V=0.078).
Conclusion
GEOPIA may provide a practical tool for identifying learner characteristics and supporting educational decision-making in medical school settings. In instructional design, personality-type data can inform group formation, activity planning, and assignment structure. In student support, the tool offers instructors and advisors a quick way to understand learners’ characteristics, which may help guide individualized counseling and promote effective learning experiences.

Academic and psychological determinants of drug‑calculation competence among nursing students in Palestine: a cross-sectional study: Ramzi Shawahna; J Educ Eval Health Prof. 2025;22:39. Published online December 29, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.39

1,283 View
151 Download

Abstract PDF Supplementary Material: Purpose
This study aimed to assess the drug-calculation skills of Palestinian nursing students and to identify academic and psychological factors influencing their performance.
Methods
A cross-sectional study was conducted among 562 nursing students in their third and fourth academic years across multiple accredited nursing schools. Data were collected using a structured questionnaire that included demographic and academic variables, prior training in drug calculations, self-rated knowledge and confidence, the Abbreviated Math Anxiety Scale, the General Self-Efficacy Scale, and 15 scenario-based drug-calculation items.
Results
The mean drug-calculation score was 79.8%±11.0% (95% confidence interval, 78.9–80.7). Female students achieved significantly higher scores than male students (B=2.20, standard error [SE]=0.24, P<0.001). Grade point average was strongly associated with performance (B=7.19, SE=0.19, P<0.001). Self-rated mathematical ability emerged as the most influential predictor (B=5.87, SE=0.08, P<0.001). Prior exposure to dedicated training in drug calculations contributed positively to performance (B=4.58, SE=0.23, P<0.001), as did self-rated confidence in drug preparation (B=1.99, SE=0.10, P<0.001). Math anxiety was inversely associated with performance (B=−0.36, SE=0.03, P<0.001), whereas general self-efficacy showed a positive association (B=0.94, SE=0.03, P<0.001).
Conclusion
Drug-calculation competence among Palestinian nursing students is shaped by academic achievement, training, confidence, math anxiety, and self-efficacy. Curricular strategies that integrate structured training, reduce math anxiety, and foster self-efficacy are essential for preparing nurses to administer medications safely.

Review

The impact of artificial intelligence-driven simulation on the development of non-technical skills in medical education: a systematic review: Sana Loubbairi, Yasmine El Moussaoui, Laila Lahlou, Imad Chakri, Hicham Nassik; J Educ Eval Health Prof. 2025;22:37. Published online November 24, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.37

4,819 View
489 Download
1 Web of Science
5 Crossref

Abstract PDF Supplementary Material

Purpose
Artificial intelligence (AI)-driven simulation is an emerging approach in healthcare education that enhances learning effectiveness. This review examined its impact on the development of non-technical skills among medical learners.
Methods
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using the following databases: Web of Science, ScienceDirect, Scopus, and PubMed. The quality of the included studies was assessed using the Mixed Methods Appraisal Tool. The protocol was previously registered in PROSPERO (CRD420251038024).
Results
Of the 1,442 studies identified in the initial search, 20 met the inclusion criteria, involving 2,535 participants. The simulators varied considerably, ranging from platforms built on symbolic AI methods to social robots powered by computational AI. Among the 15 AI-driven simulators, 10 used ChatGPT or its variants as virtual patients. Several studies evaluated multiple non-technical skills simultaneously. Communication and clinical reasoning were the most frequently assessed skills, appearing in 12 and 6 studies, respectively, which generally reported positive outcomes. Improvements were also noted in decision-making, empathy, self-confidence, critical thinking, and problem-solving. In contrast, emotional regulation, assessed in a single study, showed no significant difference. Notably, none of the studies examined reflection, reflective practice, teamwork, or leadership.
Conclusion
AI-driven simulation shows substantial potential for enhancing non-technical skills in medical education, particularly communication and clinical reasoning. However, its effects on several other non-technical skills remain unclear. Given heterogeneity in study designs and outcome measures, these findings should be interpreted cautiously. These considerations highlight the need for further research to support integrating this innovative approach into medical curricula.

Citations

Citations to this article as recorded by

The Effect of Infection Control Session on Nursing Students' Knowledge and Compliance with Standard Precautions at Hassan College of Nursing, Swat
Ahmad Ullah, Hammad Ullah Khan, Zia Ullah Khan, Numan Khan, Shah Hussain
medtigo Journal of Medicine.2026;[Epub] CrossRef
Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation
Sami Ur Rahman, Kulsoom Nadir, Muhammad Ilyas, Mehar Nigar, Anwar Khan, Abdur Rahman, Shah Hussain
medtigo Journal of Medicine.2026;[Epub] CrossRef
Advances in evaluating and delivering nontechnical skills training: The use of simulation, robotics, artificial intelligence and virtual reality
Ravanth Baskaran, Aditya Singh, Bhaskar Kumar Somani
Current Opinion in Urology.2026;[Epub] CrossRef
La formación en medicina interna en la era de la inteligencia artificial
J. García-Alegría, D. Ruiz-Hidalgo, M. Rodríguez-Carballeira
Revista Clínica Española.2026; : 502609. CrossRef
Effectiveness of a “5E+AI” teaching model on science communication in medical students: a contribution to global health literacy
Huiru Dai, Minling Liu, Tingwei Li, Jiancheng Wang, Shuo Fang
Frontiers in Public Health.2026;[Epub] CrossRef

Technical report

Feasibility of applying computerized adaptive testing to the Clinical Medical Science Comprehensive Examination in Korea: a psychometric study: Jeongwook Choi, Sung-Soo Jung, Eun Kwang Choi, Kyung Sik Kim, Dong Gi Seo; J Educ Eval Health Prof. 2025;22:29. Published online October 1, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.29

1,798 View
212 Download

Abstract PDF Supplementary Material: Purpose
This study aimed to investigate the feasibility of transitioning the Clinical Medical Science Comprehensive Examination (CMSCE) to computerized adaptive testing (CAT) in Korea, thereby providing greater opportunities for medical students to accurately compare their clinical competencies with peers nationwide and to monitor their own progress.
Methods
A medical self-assessment using CAT was conducted from March to June 2023, involving 1,541 medical students who volunteered from 40 medical colleges in Korea. An item bank consisting of 1,145 items from previously administered CMSCE examinations (2019–2021) hosted by the Medical Education Assessment Corporation was established. Items were selected through 2-stage filtering, based on classical test theory (discrimination index above 0.15) and item response theory (discrimination parameter estimates above 0.6 and difficulty parameter estimates between –5 and +5). Maximum Fisher information was employed as the item selection method, and maximum likelihood estimation was used for ability estimation.
Results
The CAT was successfully administered without significant issues. The stopping rule was set at a standard error of measurement of 0.25, with a maximum of 50 items for ability estimation. The mean ability score was 0.55, with an average of 28 items administered per student. Students at extreme ability levels reached the maximum of 50 items due to the limited availability of items at appropriate difficulty levels.
Conclusion
The medical self-assessment CAT, the first of its kind in Korea, was successfully implemented nationwide without significant problems. These results indicate strong potential for expanding the use of CAT in medical education assessments.

Research articles

Performance of ChatGPT-4 on the French Board of Plastic Reconstructive and Aesthetic Surgery written exam: a descriptive study: Emma Dejean-Bouyer, Anoujat Kanlagna, François Thuau, Pierre Perrot, Ugo Lancien; J Educ Eval Health Prof. 2025;22:27. Published online September 30, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.27

1,924 View
172 Download

Abstract PDF Supplementary Material: Purpose
This study aims to evaluate the performance of Chat Generative Pre-Trained Transformer 4 (ChatGPT-4) on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination and to assess its role as a supplementary resource in helping residents prepare for the qualification examination in plastic surgery.
Methods
This descriptive study evaluated ChatGPT-4’s performance on 213 items from the October 2024 French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Responses were assessed for accuracy, logical reasoning, internal and external information use, and were categorized for fallacies by independent reviewers. Statistical analyses included chi-square tests and Fisher’s exact test for significance.
Results
ChatGPT-4 answered all questions across the 10 modules, achieving an overall accuracy rate of 77.5%. The model applied logical reasoning in 98.1% of the questions, utilized internal information in 94.4%, and incorporated external information in 91.1%.
Conclusion
ChatGPT-4 performs satisfactorily on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Its accuracy met the minimum passing standards for the exam. While responses generally align with expected knowledge, careful verification remains necessary, particularly for questions involving image interpretation. As artificial intelligence continues to evolve, ChatGPT-4 is expected to become an increasingly reliable tool for medical education. At present, it remains a valuable resource for assisting plastic surgery residents in their training.

Validity of the formative physical therapy Student and Clinical Instructor Performance Assessment Instrument in the United States: a quasi-experimental, time-series study: Sean Gallivan, Jamie Bayliss; J Educ Eval Health Prof. 2025;22:26. Published online September 26, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.26

2,091 View
181 Download

Abstract PDF Supplementary Material: Purpose
The aim of this study was to assess the validity of the Student and Clinical Instructor Performance Instrument (SCIPAI), a novel formative tool used in physical therapist education to assess student and clinical instructor (CI) performance throughout clinical education experiences (CEEs). The researchers hypothesized that the SCIPAI would demonstrate concurrent, predictive, and construct validity while offering additional contemporary validity evidence.
Methods
This quasi-experimental, time-series study had 811 student-CI pairs complete 2 SCIPAIs before after CEE midpoint, and an endpoint Clinical Performance Instrument (CPI) during beginning to terminal CEEs in a 1-year period. Spearman rank correlation analyses used final SCIPAI and CPI like-item scores to assess concurrent validity; and earlier SCIPAI and final CPI like-item scores to assess predictive validity. Construct validity was assessed via progression of student and CI performance scores within CEEs using Wilcoxon signed-rank testing. No randomization/grouping of subjects occurred.
Results
Moderate correlation existed between like final SCIPAI and CPI items (P<0.005) and between some like items of earlier SCIPAIs and final CPIs (P<0.005). Student performance scores demonstrated progress from SCIPAIs 1 to 4 within CEEs (P<0.005). While a greater number of CIs demonstrated progression rather than regression in performance from SCIPAI 1 to SCIPAI 4, the greater magnitude of decreases in CI performance contributed to an aggregate ratings decrease of CI performance (P<0.005).
Conclusion
The SCIPAI demonstrates concurrent, predictive, and construct validity when used by students and CIs to rate student performance at regular points throughout clinical education experiences.

Comparing generative artificial intelligence platforms and nursing student performance on a women’s health nursing examination in Korea: a Rasch model approach: Eun Jeong Ko, Tae Kyung Lee, Geum Hee Jeong; J Educ Eval Health Prof. 2025;22:23. Published online September 5, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.23

2,528 View
233 Download
2 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Purpose
This psychometric study aimed to compare the ability parameter estimates of generative artificial intelligence (AI) platforms with those of nursing students on a 50-item women’s health nursing examination at Hallym University, Korea, using the Rasch model. It also sought to estimate item difficulty parameters and evaluate AI performance across varying difficulty levels.
Methods
The exam, consisting of 39 multiple-choice items and 11 true/false items, was administered to 111 fourth-year nursing students in June 2023. In December 2024, 6 generative AI platforms (GPT-4o, ChatGPT free version, Claude.ai, Clova X, Mistral.ai, Google Gemini) completed the same items. The responses were analyzed using the Rasch model to estimate the ability and difficulty parameters. Unidimensionality was verified by the Dimensionality Evaluation to Enumerate Contributing Traits (DETECT), and analyses were conducted using the R packages irtQ and TAM.
Results
The items satisfied unidimensionality (DETECT=–0.16). Item difficulty parameter estimates ranged from –3.87 to 1.96 logits (mean=–0.61), with a mean difficulty index of 0.79. Examinees’ ability parameter estimates ranged from –0.71 to 3.15 logits (mean=1.17). GPT-4o, ChatGPT free version, and Claude.ai outperformed the median student ability (1.09 logits), scoring 2.68, 2.34, and 2.34, respectively, while Clova X, Mistral.ai, and Google Gemini exhibited lower scores (0.20, –0.12, 0.80). The test information curve peaked below θ=0, indicating suitability for examinees with low to average ability.
Conclusion
Advanced generative AI platforms approximated the performance of high-performing students, but outcomes varied. The Rasch model effectively evaluated AI competency, supporting its potential utility for future AI performance assessments in nursing education.

Citations

Citations to this article as recorded by

Eroding scholarly integrity: Confronting the misuse of generative AI in nursing education
Kechi Iheduru-Anderson
Nurse Education Today.2026; 164: 107145. CrossRef

Brief Report

Effectiveness of interprofessional education enhanced by live consultation observations for healthcare students and new professionals in Singapore: a retrospective cross-sectional study: Lynette Mei Lim Goh, Wai Leong Chiu, Sky Wei Chee Koh; J Educ Eval Health Prof. 2025;22:21. Published online August 21, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.21

4,069 View
196 Download

Abstract Supplementary Material: This study aims to evaluate whether incorporating live consultation observations into interprofessional education (IPE) improves learning evaluation scores among healthcare professionals and students. A retrospective cross-sectional analysis was conducted using evaluation data from AHP IPE sessions held from January 2020 to December 2023 across 7 primary care clinics in Singapore. Evaluation scores were compared between sessions with facilitated discussions only (n=667) and sessions with additional live consultation observations (n=501). Logistic regression was used to analyze factors associated with perfect evaluation scores. Sessions that included live consultations were significantly more likely to achieve perfect evaluation scores (odds ratio [OR], 1.68; 95% confidence interval [CI], 1.27–2.22). Nursing/care coordinator and allied health professions (OR 2.07 and 1.76 respectively) were significantly more likely to give perfect scores compared to medical professions. Healthcare professionals were also more likely to give perfect scores than students (OR, 1.52; 95% CI,1.08–2.14), indicating enhanced perceived effectiveness. These findings support the use of experiential learning strategies to optimize interprofessional training outcomes.

Research articles

Correlation between task-based checklists and global rating scores in undergraduate objective structured clinical examinations in Saudi Arabia: a 1-year comparative study: Uzma Khan, Yasir Naseem Khan; J Educ Eval Health Prof. 2025;22:19. Published online June 19, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.19

8,899 View
284 Download
1 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
This study investigated the correlation between task-based checklist scores and global rating scores (GRS) in objective structured clinical examinations (OSCEs) for fourth-year undergraduate medical students and aimed to determine whether both methods can be reliably used in a standard setting.
Methods
A comparative observational study was conducted at Al Rayan College of Medicine, Saudi Arabia, involving 93 fourth-year students during the 2023–2024 academic year. OSCEs from 2 General Practice courses were analyzed, each comprising 10 stations assessing clinical competencies. Students were scored using both task-specific checklists and holistic 5-point GRS. Reliability was evaluated using Cronbach’s α, and the relationship between the 2 scoring methods was assessed using the coefficient of determination (R2). Ethical approval and informed consent were obtained.
Results
The mean OSCE score was 76.7 in Course 1 (Cronbach’s α=0.85) and 73.0 in Course 2 (Cronbach’s α=0.81). R2 values varied by station and competency. Strong correlations were observed in procedural and management skills (R2 up to 0.87), while weaker correlations appeared in history-taking stations (R2 as low as 0.35). The variability across stations highlighted the context-dependence of alignment between checklist and GRS methods.
Conclusion
Both checklists and GRS exhibit reliable psychometric properties. Their combined use improves validity in OSCE scoring, but station-specific application is recommended. Checklists may anchor pass/fail decisions, while GRS may assist in assessing borderline performance. This hybrid model increases fairness and reflects clinical authenticity in competency-based assessment.

Citations

Citations to this article as recorded by

The effectiveness of the “Early clinical exposure” course based on narrative medicine in cultivating the professional qualities of undergraduates in clinical medicine: a mixed-methods study
Zhao Li, Huijuan Cai, Xiaolin Yang, Jingsong Lin, Wenhua Cao, Peng Zhang, Jing Ren, Dayong Zheng
BMC Medical Education.2026;[Epub] CrossRef
Agreement and reliability of global rating versus checklist scores in a high-stakes undergraduate OSCE in Rwanda
Olayinka Rasheed Ibrahim, Natalie McCall, Abebe Bekele, Biniam Ewnte Zelelew, Oluwaseun Ojomo, Anteneh Gadisa Belachew, Equlinet Misganaw Amare, Zelalem Mengistu Gashaw, Birhanu Abera Ayana, Ariane Nina Ndayikeje
BMC Medical Education.2026;[Epub] CrossRef

Mixed reality versus manikins in basic life support simulation-based training for medical students in France: the mixed reality non-inferiority randomized controlled trial: Sofia Barlocco De La Vega, Evelyne Guerif-Dubreucq, Jebrane Bouaoud, Myriam Awad, Léonard Mathon, Agathe Beauvais, Thomas Olivier, Pierre-Clément Thiébaud, Anne-Laure Philippon; J Educ Eval Health Prof. 2025;22:15. Published online May 12, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.15

5,042 View
393 Download
3 Web of Science
4 Crossref

Abstract PDF Supplementary Material

Purpose
To compare the effectiveness of mixed reality with traditional manikin-based simulation in basic life support (BLS) training, testing the hypothesis that mixed reality is non-inferior to manikin-based simulation.
Methods
A non-inferiority randomized controlled trial was conducted. Third-year medical students were randomized into 2 groups. The mixed reality group received 32 minutes of individual training using a virtual reality headset and a torso for chest compressions (CC). The manikin group participated in 2 hours of group training consisting of theoretical and practical sessions using a low-fidelity manikin. The primary outcome was the overall BLS performance score, assessed at 1 month through a standardized BLS scenario using a 10-item assessment scale. The quality of CC, student satisfaction, and confidence levels were secondary outcomes and assessed through superiority analyses.
Results
Data from 155 participants were analyzed, with 84 in the mixed reality group and 71 in the manikin group. The mean overall BLS performance score was 6.4 (mixed reality) vs. 6.5 (manikin), (mean difference, –0.1; 95% confidence interval [CI], –0.45 to +∞). CC depth was greater in the manikin group (50.3 mm vs. 46.6 mm; mean difference, –3.7 mm; 95% CI, –6.5 to –0.9), with 61.2% achieving optimal depth compared to 43.8% in the mixed reality group (mean difference, 17.4%; 95% CI, –29.3 to –5.5). Satisfaction was higher in the mixed reality group (4.9/5 vs. 4.7/5 in the manikin group; difference, 0.2; 95% CI, 0.07 to 0.33), as was confidence in performing BLS (3.9/5 vs. 3.6/5; difference, 0.3; 95% CI, 0.11 to 0.58). No other significant differences were observed for secondary outcomes.
Conclusion
Mixed reality is non-inferior to manikin simulation in terms of overall BLS performance score assessed at 1 month.

Citations

Citations to this article as recorded by

Enhancing virtual reality applications for adult basic life support: insights from a comparative analysis
Nino Fijačko, Benjamin S. Abella, Vinay M. Nadkarni, Špela Metličar, Anne-Astrid Agten, Robert Greif
Virtual Reality.2026;[Epub] CrossRef
Enhancing STEM and STEAM Education at the Grade Level Through Mixed Reality Applications: A Meta-analytical Study
Segun Michael Ojetunde, Umesh Ramnarain
Canadian Journal of Science, Mathematics and Technology Education.2026;[Epub] CrossRef
Collocated mixed reality for basic life support training in medical students: a randomised pilot feasibility trial
David Maiershon, Jonathan Giron, Vered Daitch, Yael Feinstein, Hadas Leibovich-Kot, Shlomit Tovbis, Tohar Levartovsky Ovadiya, Hadar Lavi, Ben Hazan, Maxim Lantsman, Alon Basker, Miri Mizrahi Reuveni
Resuscitation Plus.2026; : 101383. CrossRef
IMPACTOS DO USO DE SIMULAÇÃO IMERSIVA NA CAPACITAÇÃO EM ATENDIMENTO À PARADA CARDIORRESPIRATÓRIA
Iago Brenner Farias Leal, Izabelly Ferreira de Andrade, Yan Carlos de Sousa Diniz, Lara Maria Ferreira Lopes Valéri Pinto, Maria Helena Vieira Pereira Marques, Francisca Evelyn Abreu de Lira, Thaís Helena Gomes de Sousa, Maria Isabelly Araújo Ferreira, A
Revista Multidisciplinar do Nordeste Mineiro.2025; 16(1): 1. CrossRef

Assessing genetic and genomic literacy concepts among Albanian nursing and midwifery students: a cross-sectional study: Elona Gaxhja, Mitilda Gugu, Angelo Dante, Armelda Teta, Armela Kapaj, Liljana Ramasaco; J Educ Eval Health Prof. 2025;22:13. Published online April 21, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.13

4,835 View
314 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to adapt and validate the Albanian version of the Genomic Nursing Concept Inventory (GNCI) and to assess the level of genomic literacy among nursing and midwifery students.
Methods
Data were collected via a monocentric online cross-sectional study using the Albanian version of the GNCI. Participants included first-, second-, and third-year nursing and midwifery students. Demographic data such as age, sex, year level, and prior exposure to genetics were collected. The Kruskal-Wallis, Mann-Whitney U, and chi-square tests were used to compare demographic characteristics and GNCI scores between groups.
Results
Among the 715 participants, most were female (88.5%) with a median age of 19 years. Most respondents (65%) had not taken a genetics course, and 83.5% had not attended any related training. The mean score was 7.49, corresponding to a scale difficulty of 24.38% correct responses.
Conclusion
The findings reveal a low foundational knowledge of genetics/genomics among future nurses and midwives. It is essential to enhance learning strategies and update curricula to prepare a competent healthcare workforce in precision health.

Citations

Citations to this article as recorded by

Genomic literacy in nursing: a systematic scoping review of the literature
Angelo Dante, Vittorio Masotta, Ilaria Paoli, Valeria Caponnetto, Maria Caples, Mari Laaksonen, Tina Kamenšek, Cristina Petrucci, Loreto Lancia
BMJ Open.2025; 15(7): e100054. CrossRef

Evaluation of a virtual objective structured clinical examination in the metaverse (Second Life) to assess the clinical skills in emergency radiology of medical students in Spain: a cross-sectional study: Alba Virtudes Perez-Baena, Teodoro Rudolphi-Solero, Rocio Lorenzo-Alvarez, Dolores Dominguez-Pinos, Miguel Jose Ruiz-Gomez, Francisco Sendra-Portero; J Educ Eval Health Prof. 2025;22:12. Published online April 21, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.12

6,507 View
2,147,483,941 Download
3 Web of Science
5 Crossref

Abstract PDF Supplementary Material

Purpose
The objective structured clinical examination (OSCE) is an effective but resource-intensive tool for assessing clinical competence. This study hypothesized that implementing a virtual OSCE in the Second Life (SL) platform in the metaverse as a cost-effective alternative will effectively assess and enhance clinical skills in emergency radiology while being feasible and well-received. The aim was to evaluate a virtual radiology OSCE in SL as a formative assessment, focusing on feasibility, educational impact, and students’ perceptions.
Methods
Two virtual 6-station OSCE rooms dedicated to emergency radiology were developed in SL. Sixth-year medical students completed the OSCE during a 1-hour session in 2022–2023, followed by feedback including a correction checklist, individual scores, and group comparisons. Students completed a questionnaire with Likert-scale questions, a 10-point rating, and open-ended comments. Quantitative data were analyzed using the Student t-test and the Mann-Whitney U test, and qualitative data through thematic analysis.
Results
In total, 163 students participated, achieving mean scores of 5.1±1.4 and 4.9±1.3 (out of 10) in the 2 virtual OSCE rooms, respectively (P=0.287). One hundred seventeen students evaluated the OSCE, praising the teaching staff (9.3±1.0), project organization (8.8±1.2), OSCE environment (8.7±1.5), training usefulness (8.6±1.5), and formative self-assessment (8.5±1.4). Likert-scale questions and students’ open-ended comments highlighted the virtual environment’s attractiveness, case selection, self-evaluation usefulness, project excellence, and training impact. Technical difficulties were reported by 13 students (8%).
Conclusion
This study demonstrated the feasibility of incorporating formative OSCEs in SL as a useful teaching tool for undergraduate radiology education, which was cost-effective and highly valued by students.

Citations

Citations to this article as recorded by

Effectiveness of VR and traditional training in medical education for mass casualty management: an OSCE-based randomized controlled trial
Zhe Li, Wan Chen, Guozheng Qiu, Lei Shi, Yutao Tang, Xibin Xu, Sanshan Zhu, Liwen Lyu
BMC Medical Education.2026;[Epub] CrossRef
ECOE virtual de radiología en el metaverso Second Life®: comparación de estudiantes de tercer y sexto curso
Francisco Sendra-Portero, Alba Virtudes Pérez-Baena, Teodoro Rudolphi-Solero, Rocío Lorenzo-Álvarez, Dolores Domínguez-Pinos, Miguel José Ruiz-Gómez
Educación Médica.2026; 27(3): 101167. CrossRef
A roadmap to implement Metaverse in education field based on ADDIE model: A systematic review
Yu Shi, Wei Wei Goh, N.Z. Jhanjhi
Computers and Education Open.2026; 10: 100365. CrossRef
Metaverse-based objective structured clinical examinations: an exploratory approach to advancing clinical competency assessment
Yeon-Ju Huh, Joon Sung Shin, Narae Yoon, Ju Whi Kim, Do Hoon Kim, Chanwoong Kim, Seoi Jeong, Yejin Yoon, Soyeon Shin, Hyoun-Joong Kong, Sun Jung Myung
Korean Journal of Medical Education.2026; 38(2): 139. CrossRef
An AI‐Driven H5P Training for Epilepsy Emergency Management Preparedness in Dental Education
İlkem Gökçe, Özlem Sürel Karabilgin Öztürkçü, Ozan Karaca, Cihan Varol, Mukadder İnci Başer Kolcu
European Journal of Dental Education.2026;[Epub] CrossRef

Empathy and tolerance of ambiguity in medical students and doctors participating in art-based observational training at the Rijksmuseum in Amsterdam, the Netherlands: a before-and-after study: Stella Anna Bult, Thomas van Gulik; J Educ Eval Health Prof. 2025;22:3. Published online January 14, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.3

6,816 View
368 Download
5 Web of Science
5 Crossref

Abstract PDF Supplementary Material

Purpose
This research presents an experimental study using validated questionnaires to quantitatively assess the outcomes of art-based observational training in medical students, residents, and specialists. The study tested the hypothesis that art-based observational training would lead to measurable effects on judgement skills (tolerance of ambiguity) and empathy in medical students and doctors.
Methods
An experimental cohort study with pre- and post-intervention assessments was conducted using validated questionnaires and qualitative evaluation forms to examine the outcomes of art-based observational training in medical students and doctors. Between December 2023 and June 2024, 15 art courses were conducted in the Rijksmuseum in Amsterdam. Participants were assessed on empathy using the Jefferson Scale of Empathy (JSE) and tolerance of ambiguity using the Tolerance of Ambiguity in Medical Students and Doctors (TAMSAD) scale.
Results
In total, 91 participants were included; 29 participants completed the JSE and 62 completed the TAMSAD scales. The results showed statistically significant post-test increases for mean JSE and TAMSAD scores (3.71 points for the JSE, ranging from 20 to 140, and 1.86 points for the TAMSAD, ranging from 0 to 100). The qualitative findings were predominantly positive.
Conclusion
The results suggest that incorporating art-based observational training in medical education improves empathy and tolerance of ambiguity. This study highlights the importance of art-based observational training in medical education in the professional development of medical students and doctors.

Citations

Citations to this article as recorded by

Understanding uncertainty and ambiguity in medicine and medical education: a narrative review with implications for training
Sarine Sarkis, Christian Raphael
Postgraduate Medical Journal.2026; 102(1207): 461. CrossRef
Observational training for surgical residents using visual arts in the museum
Thomas M. van Gulik, Stella A. Bult, Pien E.J. de Ruiter, Floortje Huizing, Alexander de Mol van Otterloo, Alexander Leijdesdorff, Sjoerd Lagarde
Surgery.2026; 190: 109843. CrossRef
Training the eye and diagnosing the canvas in the Museum ‘A perspective on art-based medical education’
T.M. van Gulik, S.A. Bult, P.E.J. de Ruiter, F. Huizing, A. Leijdesdorff, S. Lagarde, A. de Mol van Otterloo
Ethics, Medicine and Public Health.2026; 34: 101243. CrossRef
Developing a Feasible Arts and Humanities Course Using Visual Thinking Strategies and Haiku Writing: A Mixed-Methods Study
Hirohisa Fujikawa, Takayuki Ando, Junji Haruta
Medical Science Educator.2025; 35(6): 3105. CrossRef
Erb’s Palsy: Visual Diagnosis in Art before Medical History?
Pien E.J. de Ruiter, Stella A. Bult, Jeroen R. Dijkstra, Thomas M. van Gulik
Gynecologic and Obstetric Investigation.2025; 91(1): 26. CrossRef

Pharmacy students’ perspective on remote flipped classrooms in Malaysia: a qualitative study: Wei Jin Wong, Shaun Wen Huey Lee, Ronald Fook Seng Lee; J Educ Eval Health Prof. 2025;22:2. Published online January 14, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.2

5,692 View
388 Download
1 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to explore pharmacy students’ perceptions of remote flipped classrooms in Malaysia, focusing on their learning experiences and identifying areas for potential improvement to inform future educational strategies.
Methods
A qualitative approach was employed, utilizing inductive thematic analysis. Twenty Bachelor of Pharmacy students (18 women, 2 men; age range, 19–24 years) from Monash University participated in 8 focus group discussions over 2 rounds during the coronavirus disease 2019 pandemic. Participants were recruited via convenience sampling. The focus group discussions, led by experienced academics, were conducted in English via Zoom, recorded, and transcribed for analysis using NVivo. Themes were identified through emergent coding and iterative discussions to ensure thematic saturation.
Results
Five major themes emerged: flexibility, communication, technological challenges, skill-based learning challenges, and time-based effects. Students appreciated the flexibility of accessing and reviewing pre-class materials at their convenience. Increased engagement through anonymous question submission was noted, yet communication difficulties and lack of non-verbal cues in remote workshops were significant drawbacks. Technological issues, such as internet connectivity problems, hindered learning, especially during assessments. Skill-based learning faced challenges in remote settings, including lab activities and clinical examinations. Additionally, prolonged remote learning led to feelings of isolation, fatigue, and a desire to return to in-person interactions.
Conclusion
Remote flipped classrooms offer flexibility and engagement benefits but present notable challenges related to communication, technology, and skill-based learning. To improve remote education, institutions should integrate robust technological support, enhance communication strategies, and incorporate virtual simulations for practical skills. Balancing asynchronous and synchronous methods while addressing academic success and socioemotional wellness is essential for effective remote learning environments.

Citations

Citations to this article as recorded by

An Empirical Investigation of Zoom Fatigue and Academic Motivation in Maritime Education: A Structural Equation Modelling Approach
Burcu Çelik Maşalacı, Elif Arslan
Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi.2026; 7(1): 241. CrossRef

Development and validation of a measurement tool to assess student perceptions of using real patients in physical therapy education at the Rocky Mountain University, the United States: a methodological study: Stacia Hall Thompson, Hina Garg, Mary Shotwell, Michelle Webb; J Educ Eval Health Prof. 2024;21:30. Published online November 7, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.30

3,745 View
234 Download
1 Web of Science

Abstract PDF Supplementary Material: Purpose
This study aimed to develop and validate the Student Perceptions of Real Patient Use in Physical Therapy Education (SPRP-PTE) survey to assess physical therapy student (SPT) perceptions regarding real patient use in didactic education.
Methods
This cross-sectional observational study developed a 48-item survey and tested the survey on 130 SPTs. Face and content validity were determined by an expert review and content validity index (CVI). Construct validity and internal consistency reliability were determined via exploratory factor analysis (EFA) and Cronbach’s α.
Results
Three main constructs were identified (value, satisfaction, and confidence), each having 4 subconstruct components (overall, cognitive, psychomotor, and affective learning). Expert review demonstrated adequate face and content validity (CVI=96%). The initial EFA of the 48-item survey revealed items with inconsistent loadings and low correlations, leading to the removal of 18 items. An EFA of the 30-item survey demonstrated 1-factor loadings of all survey constructs except satisfaction and the entire survey. All constructs had adequate internal consistency (Cronbach’s α >0.85).
Conclusion
The SPRP-PTE survey provides a reliable and valid way to assess student perceptions of real patient use. Future studies are encouraged to validate the SPRP-PTE survey further.

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study: Sebastian Ebel, Constantin Ehrengut, Timm Denecke, Holger Gößmann, Anne Bettina Beeskow; J Educ Eval Health Prof. 2024;21:21. Published online August 20, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.21

6,609 View
358 Download
17 Web of Science
16 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology.
Methods
GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024.
Results
GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible.
Conclusion
GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.

Citations

Citations to this article as recorded by

When AI meets medical assessment: A comparative study of GPT-4O and Claude 3 Opus in China's standardized resident physician examinations
Wenjie Zhong, Yidan Hu, Ruiqiang Su, Lingcong Xu, Yating Chen, Niezhenghao He, Caiyuan Liu, Ke Xu, Mao Zhao, Wenao Liao, Wei Zhang, Jiang Hu, Fei Wang, Haowen Cui
Computers in Human Behavior Reports.2026; 21: 100974. CrossRef
The current status and future prospects of artificial intelligence education in residency training
Hongsen Zhang, Kun Qian, Jing Wang, Chuansheng Zheng
Frontiers in Education.2026;[Epub] CrossRef
Comparative analysis of multimodal large language models GPT-4o and o1 versus clinicians in clinical case challenge questions: Retrospective cross-sectional study
Jaewon Jung, Hyunjae Kim, SungA Bae, Jin Young Park
Medicine.2026; 105(4): e47071. CrossRef
Validity of AI-generated multiple-choice questions in medical education: a systematic review
Yavuz Selim Kıyak, Abdullah Bedir Kaya, Emre Emekli
Postgraduate Medical Journal.2026;[Epub] CrossRef
Evaluation of large language models in a national orthopaedic proficiency examination: Implications for health informatics and medical education
Bünyamin Arı
Health Informatics Journal.2026;[Epub] CrossRef
Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease
Yue Wang, Shuo Yang, Chengcheng Zeng, Yingwei Xie, Ya Shen, Jian Li, Xiao Huang, Ruili Wei, Yuqing Chen
Frontiers in Medicine.2025;[Epub] CrossRef
Solving Complex Pediatric Surgical Case Studies: A Comparative Analysis of Copilot, ChatGPT-4, and Experienced Pediatric Surgeons' Performance
Richard Gnatzy, Martin Lacher, Michael Berger, Michael Boettcher, Oliver J. Deffaa, Joachim Kübler, Omid Madadi-Sanjani, Illya Martynov, Steffi Mayer, Mikko P. Pakarinen, Richard Wagner, Tomas Wester, Augusto Zani, Ophelia Aubert
European Journal of Pediatric Surgery.2025; 35(05): 382. CrossRef
Preliminary assessment of large language models’ performance in answering questions on developmental dysplasia of the hip
Shiwei Li, Jun Jiang, Xiaodong Yang
Journal of Children's Orthopaedics.2025; 19(3): 207. CrossRef
AI and Interventional Radiology: A Narrative Review of Reviews on Opportunities, Challenges, and Future Directions
Andrea Lastrucci, Nicola Iosca, Yannick Wandael, Angelo Barra, Graziano Lepri, Nevio Forini, Renzo Ricci, Vittorio Miele, Daniele Giansanti
Diagnostics.2025; 15(7): 893. CrossRef
Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination
Dingyuan Luo, Mengke Liu, Runyuan Yu, Yulian Liu, Wenjun Jiang, Qi Fan, Naifeng Kuang, Qiang Gao, Tao Yin, Zuncheng Zheng
Scientific Reports.2025;[Epub] CrossRef
Evaluating Large Language Models for Preoperative Patient Education in Superior Capsular Reconstruction: Comparative Study of Claude, GPT, and Gemini
Yukang Liu, Hua Li, Jianfeng Ouyang, Zhaowen Xue, Min Wang, Hebei He, Bin Song, Xiaofei Zheng, Wenyi Gan
JMIR Perioperative Medicine.2025; 8: e70047. CrossRef
Evaluating ChatGPT's performance across radiology subspecialties: A meta-analysis of board-style examination accuracy and variability
Dan Nguyen, Grace Hyun J. Kim, Arash Bedayat
Clinical Imaging.2025; 125: 110551. CrossRef
Performance of ChatGPT-4 on the French Board of Plastic Reconstructive and Aesthetic Surgery written exam: a descriptive study
Emma Dejean-Bouyer, Anoujat Kanlagna, François Thuau, Pierre Perrot, Ugo Lancien
Journal of Educational Evaluation for Health Professions.2025; 22: 27. CrossRef
Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review
Junseok Kang, Jihyun Ahn
Ewha Medical Journal.2025; 48(4): e53. CrossRef
From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance
Markus Kipp
Information.2024; 15(9): 543. CrossRef
Performance of ChatGPT and Bard on the medical licensing examinations varies across different cultures: a comparison study
Yikai Chen, Xiujie Huang, Fangjie Yang, Haiming Lin, Haoyu Lin, Zhuoqun Zheng, Qifeng Liang, Jinhai Zhang, Xinxin Li
BMC Medical Education.2024;[Epub] CrossRef

Impact of a change from A–F grading to honors/pass/fail grading on academic performance at Yonsei University College of Medicine in Korea: a cross-sectional serial mediation analysis: Min-Kyeong Kim, Hae Won Kim; J Educ Eval Health Prof. 2024;21:20. Published online August 16, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.20; Correction in: J Educ Eval Health Prof 2024;21(0):35

6,399 View
411 Download
2 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to explore how the grading system affected medical students’ academic performance based on their perceptions of the learning environment and intrinsic motivation in the context of changing from norm-referenced A–F grading to criterion-referenced honors/pass/fail grading.
Methods
The study involved 238 second-year medical students from 2014 (n=127, A–F grading) and 2015 (n=111, honors/pass/fail grading) at Yonsei University College of Medicine in Korea. Scores on the Dundee Ready Education Environment Measure, the Academic Motivation Scale, and the Basic Medical Science Examination were used to measure overall learning environment perceptions, intrinsic motivation, and academic performance, respectively. Serial mediation analysis was conducted to examine the pathways between the grading system and academic performance, focusing on the mediating roles of student perceptions and intrinsic motivation.
Results
The honors/pass/fail grading class students reported more positive perceptions of the learning environment, higher intrinsic motivation, and better academic performance than the A–F grading class students. Mediation analysis demonstrated a serial mediation effect between the grading system and academic performance through learning environment perceptions and intrinsic motivation. Student perceptions and intrinsic motivation did not independently mediate the relationship between the grading system and performance.
Conclusion
Reducing the number of grades and eliminating rank-based grading might have created an affirming learning environment that fulfills basic psychological needs and reinforces the intrinsic motivation linked to academic performance. The cumulative effect of these 2 mediators suggests that a comprehensive approach should be used to understand student performance.

Citations

Citations to this article as recorded by

Alternative grading approaches in health professions education: a scoping review protocol
Elena Wong Espiritu, Aine O’Connor, Sara Blass, Kathryn L. Dambrino, Angela Shelton Clauson
JBI Evidence Synthesis.2025; 23(11): 2301. CrossRef
Erratum: Impact of a change from A–F grading to honors/pass/fail grading on academic performance at Yonsei University College of Medicine in Korea: a cross-sectional serial mediation analysis

Journal of Educational Evaluation for Health Professions.2024; 21: 35. CrossRef

Special article on the 20th anniversary of the journal

Comparison of real data and simulated data analysis of a stopping rule based on the standard error of measurement in computerized adaptive testing for medical examinations in Korea: a psychometric study: Dong Gi Seo, Jeongwook Choi, Jinha Kim; J Educ Eval Health Prof. 2024;21:18. Published online July 9, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.18

4,520 View
380 Download
2 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to compare and evaluate the efficiency and accuracy of computerized adaptive testing (CAT) under 2 stopping rules (standard error of measurement [SEM]=0.3 and 0.25) using both real and simulated data in medical examinations in Korea.
Methods
This study employed post-hoc simulation and real data analysis to explore the optimal stopping rule for CAT in medical examinations. The real data were obtained from the responses of 3rd-year medical students during examinations in 2020 at Hallym University College of Medicine. Simulated data were generated using estimated parameters from a real item bank in R. Outcome variables included the number of examinees’ passing or failing with SEM values of 0.25 and 0.30, the number of items administered, and the correlation. The consistency of real CAT result was evaluated by examining consistency of pass or fail based on a cut score of 0.0. The efficiency of all CAT designs was assessed by comparing the average number of items administered under both stopping rules.
Results
Both SEM 0.25 and SEM 0.30 provided a good balance between accuracy and efficiency in CAT. The real data showed minimal differences in pass/fail outcomes between the 2 SEM conditions, with a high correlation (r=0.99) between ability estimates. The simulation results confirmed these findings, indicating similar average item numbers between real and simulated data.
Conclusion
The findings suggest that both SEM 0.25 and 0.30 are effective termination criteria in the context of the Rasch model, balancing accuracy and efficiency in CAT.

Citations

Citations to this article as recorded by

AI-enhanced adaptive testing with cognitive diagnostic feedback and its association with performance in undergraduate surgical education: a pilot study
Nuno Silva Gonçalves, Carlos Collares, José Miguel Pêgo
Frontiers in Behavioral Neuroscience.2026;[Epub] CrossRef
Feasibility of applying computerized adaptive testing to the Clinical Medical Science Comprehensive Examination in Korea: a psychometric study
Jeongwook Choi, Sung-Soo Jung, Eun Kwang Choi, Kyung Sik Kim, Dong Gi Seo
Journal of Educational Evaluation for Health Professions.2025; 22: 29. CrossRef

Review

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review: Xiaojun Xu, Yixiao Chen, Jing Miao; J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.6

25,830 View
982 Download
103 Web of Science
127 Crossref

Abstract PDF Supplementary Material

Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

Citations

Citations to this article as recorded by

Reliability of ChatGPT-4o in analysing medical data: a test case study on patients at risk for limb amputation
Liat Toderis, Iris Reychav, Roger McHaney, Bernice Oberman, Chen Speter, Ronen Loebstein
Health Systems.2026; 15(2): 125. CrossRef
Large language models in education: a systematic review of empirical applications, benefits, and challenges
Yuhong Shi, Kun Yu, Yifei Dong, Fang Chen
Computers and Education: Artificial Intelligence.2026; 10: 100529. CrossRef
Evaluating the Insights of ChatGPT, Gemini and Expert Surgeons in Revision Rhinoplasty Consultation
Shahin Bastaninejad, Samira Alipour, Luiz Carlos Ishida, Mahdieh Mohebbi, Benyamin Mousavi-asl, Farrokh Heidari, Habib Azimi
Aesthetic Plastic Surgery.2026; 50(11): 3831. CrossRef
Large Language Model Applications in the Algebra Domain: A Systematic Review
Yajie Song, Yimei Zhang, Doina Precup, Reihaneh Rabbany, Maria Cutumisu
Technology, Knowledge and Learning.2026; 31(2): 685. CrossRef
Conversational AI agents in education: an umbrella review of current utilization, challenges, and future directions for ethical and responsible use
Amrita Ganguly, Nafisa Mehjabin, Aqdas Malik, Aditya Johri
AI and Ethics.2026;[Epub] CrossRef
Generative artificial intelligence in mental health: A preliminary study on automating materials development for cognitive bias modification
Che-Wei Hsu, Mia Cochrane, Sasini Bambarawana
International Journal of Mental Health.2026; : 1. CrossRef
Medical Student Experiences With ChatGPT: National Cross-Sectional Study
Alan Yuesheng Xu, Skye Speakman, Vincent Salvatore Piranio, Robert Medina, Michelle Liu, Chris Lamprecht, Nicolas Abchee, Meghan Brennan
JMIR Formative Research.2026; 10: e76838. CrossRef
Large Language Models Evaluation of Medical Licensing Examination Using GPT-4.0, ERNIE Bot 4.0, and GPT-4o
Luoyu Lian, Xin Luo, Kavimbi Chipusu, Muhammad Awais Ashraf, Kelvin K. L. Wong, Wenjun Zhang
Bioengineering.2026; 13(1): 113. CrossRef
From “teaching by word and deed” to “intelligent mentorship”: ethical reconsiderations of AI-enabled medical education — lessons from China
Zhitao Hou, Jing Chen, Hongwei Guo
Frontiers in Medicine.2026;[Epub] CrossRef
Artificial intelligence in medical ethics education: a descriptive study of eight models in multiple choice question generation
John Obeid, Christopher Bobier, Alex Gillham, Adam Omelianchuk, Daniel Hurst
BMC Medical Education.2026;[Epub] CrossRef
Performance of ChatGPT-4o, Gemini 2.0 Pro, and DeepSeek-V3 in Patient-Facing Information on Chest Wall Deformities: A Comparative Evaluation of Accuracy, RELIABILITY, and Reproducibility
Deniz Oke, Ozge Gulsum Illeez, Esra Giray, Betül Çiftçi
Diagnostics.2026; 16(4): 589. CrossRef
Current concerns and future directions of large language model ChatGPT in medicine: a machine-learning-driven global-scale bibliometric analysis
Song-Bin Guo, Deng-Yao Liu, Xiao-Jie Fang, Yuan Meng, Zhen-Zhong Zhou, Jing Li, Mei Li, Li-Ling Luo, Hai-Long Li, Xiu-Yu Cai, Wei-Juan Huang, Xiao-Peng Tian
International Journal of Surgery.2026; 112(2): 2805. CrossRef
Postgraduate General Practice Training Under Early Clinical Responsibility: A Narrative Review on System-Based Supervision and the Supportive Role of Artificial Intelligence
Christian J. Wiedermann, Giuliano Piccoliori, Pietro Murali, Cristina Pizzini, Doris Hager von Strobele Prainsack
Healthcare.2026; 14(4): 503. CrossRef
Comparing ChatGPT, Gemini, and Emerging LLMs in Low-Resource Educational Settings: Reasoning Quality, Consistency, and Explainability
Aytug Onan, Arbi Haza Nasution, Tugba Celikten, Pinar Cetin
IEEE Access.2026; 14: 32807. CrossRef
From Hallucination to Precision: A Longitudinal Analysis of Reference Accuracy and Plagiarism in AI-Generated Medical Literature (2024–2026)
Mevlüt Okan Aydin, Alper Vatansever, Sezer Erer Kafa
Uludağ Üniversitesi Tıp Fakültesi Dergisi.2026; 52: 1870116. CrossRef
Comparison and Review of Different Versions of OpenAI ChatGPT, Anthropic Claude and Google Gemini Large Language Models' Performance on Endodontics Questions in the Turkish Dentistry Specialization Exam
Anil Ozgun Karatekin
European Journal of Dental Education.2026;[Epub] CrossRef
Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis
Nora Arvai, Bertalan Meskó, Gellért Katonai
JMIR Medical Education.2026; 12: e85373. CrossRef
From policy to practice: editorial leadership in the age of AI-assisted science
Whitley Stone, Matthew Garver, Samantha Johnson, Jennifer Caputo, Adam Ibrahim, Dustin Davis, James Navalta
Sport, Education and Society.2026; : 1. CrossRef
Implementation of artificial intelligence in the 2025 medical parasitology course at Hallym University
Eun Hee Ha
Journal of Educational Evaluation for Health Professions.2026; 23: 4. CrossRef
Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study
Zeynep Nisa Karakoyun, Mustafa Deniz Yörük, Mehmed Emre Özdemir, Mehmet İlkay Koşar
BMC Medical Education.2026;[Epub] CrossRef
The Evolving Role of AI in Simulation-Based Medical Education: A Narrative Review
Selina Hasan, Ayesha Ahmed, Faisal Ismail
Advances in Medical Education and Practice.2026; Volume 17: 1. CrossRef
Evaluating the performance of large language models versus prosthodontic residents on the 2024 and 2025 National Prosthodontic Resident Examination
Soni Prasad, Qiao Fang, Merve Koseoglu, Marwa Shembesh, Maryam Gheisarifar, Cortino Sukotjo
The Journal of Prosthetic Dentistry.2026;[Epub] CrossRef
Deciphering AI-chatbots utilization in clinical practice, research and education among Chinese medical students in generative AI era: a cross-sectional study
Wenyi Gan, Yukang Liu, Anqi Lin, Bufu Tang, Aimin Jiang, Chang Qi, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Mingjia Xiao, Guangdi Chu, Zaoqu Liu, Jiarui Xie, Quan Chen, Yiyi Zhang, Peng Luo
BMC Medical Education.2026;[Epub] CrossRef
AI at the bedside: Randomised controlled trial of ChatGPT’s impact on student performance in real-patient clinical exams
Haroon Saloojee, Michael C. Gramanie, Rhodasi Mwale, Bruce A. Bassett, Shabir A. Madhi, Ismail S. Kalla
Medical Teacher.2026; : 1. CrossRef
Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training
Ligang Jiang, Xin Jiang, Wencan Wu, Fangzheng Jiang
Frontiers in Public Health.2026;[Epub] CrossRef
Generative Artificial Intelligence and the Problem of Authorship and Personal Voice in Academic Writing
Sousen Elbouri, Wahbi Albasyouni
Review of Artificial Intelligence in Education.2026; 7: e083. CrossRef
Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation
Musarrat Zeba, Abdullah Al Mamun, Kishoar Jahan Tithee, Debopom Sutradhar, Mohaimenul Azam Khan Raiaan, Saddam Mukta, Reem E. Mohamed, Md Rafiqul Islam, Yakub Sebastian, Mukhtar Hussain, Sami Azam
Expert Systems with Applications.2026; 329: 132966. CrossRef
The use of generative AI to improve the speaking skills of Chinese EFL learners in the vocational education context
Xingju Chen, Bin Zou, Chenghao Wang
Journal of China Computer-Assisted Language Learning.2026;[Epub] CrossRef
A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends
Yuanyuan Fan, Xun Zheng, Guang Yang, Pengyu Li, Yanhao Ran, Tianfeng Xu, Tao Wei
Medicine.2026; 105(22): e49128. CrossRef
Clinical Evaluation of ChatGPT-5.3 Responses to Patient-Oriented Questions on Scoliosis: A Multidimensional Expert Analysis
Muhsin Doran
Healthcare.2026; 14(11): 1563. CrossRef
Sentiment Differences in Resolving Moral Dilemmas: An Analysis of Student Attitudes and Large Language Models
Agnieszka Zok, Jadwiga Wiertlweska-Bielarz, Marcin Moskalewicz
Journal of Academic Ethics.2026;[Epub] CrossRef
Generative Artificial Intelligence-driven orthodontic education practices
Menghan Zhang, Yuzhi Yang, Yan lv, Yanfang Yu, Sihui Hu, Ziyuan Yang, Zhiwei Wang, Mengjie Wu
BMC Medical Education.2026;[Epub] CrossRef
Large language models as data-driven engines for benchmarking preventive and clinical knowledge in Chinese dental examinations
Yong Zeng, Xinyi Hu, Wei Liu, Ke Deng, Meiqin Zhou, Yao Wang, Ling Ma, Qi Liu, He Meng
Frontiers in Oral Health.2026;[Epub] CrossRef
Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial
Can Zhao, Jianzhong Zhu, Jianhui Liu, Wentao Zhao, Yin Pang
BMC Medical Education.2026;[Epub] CrossRef
Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review
Jing Chang, Ruotong Peng, Xi Chen, Yishu Zhu, Ruting Miao, Zeng Cao, Hui Feng
Journal of Medical Internet Research.2026; 28: e84726. CrossRef
An Examination of Mathematics Teachers’ Views on Differentiated and Individualized Education Programs for Gifted Students Developed with Artificial Intelligence
Burak Karabey, Arzunur Çakmak, Yağmur Günay
Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi.2026; 20(1): 357. CrossRef
The Use of Artificial Intelligence Technologies in Higher Medical Education: Benefits, Possible Risks and Ways to Improve
O. Voloshyna
Lviv clinical bulletin.2026; (2 (54)): 44. CrossRef
The Role of Artificial Intelligence in Nephrology Education
Jing Miao, Charat Thongprayoon, Wisit Cheungpasitporn
Advances in Kidney Disease and Health.2026;[Epub] CrossRef
The digital therapist? LLMs and the future of clinical dialogue
Edward Ruoyang Shi, Ran Pei, Lluís Barceló-Coblijn
Language and Health.2026; 4(2): 100086. CrossRef
Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching
Yuan Shen, Yuxiao Chen, Mengyao Li, Xiang Gu, Ai Zhuang
Frontiers in Medicine.2026;[Epub] CrossRef
The impact of generative artificial intelligence on clinical skills and knowledge acquisition in medical undergraduates: a systematic review and meta-analysis
Guanli Xie, Jianglong Liao, Xiaoxia Tang, Han Fu, Duo Liu, Li Deng, Tao Wang, Xin Hong
BMC Medical Education.2026;[Epub] CrossRef
The Perceived Effectiveness of AI‐Powered Tools in Undergraduate Anatomy Education: A Cross‐Sectional Multifaceted Evaluation
Ayisha Almamari, Mohamed Al‐Farsi, Abdul‐Rahman Almamari, Hussein Abdellatif
Clinical Anatomy.2026;[Epub] CrossRef
An exploratory study of the use of artificial intelligence-based virtual patients to enhance dentist-patient communication training
Yixuan Xie, Zhanpeng Ou, Yuanding Huang, Hong Huang, Xiongwen Ran, Hongwei Dai, Bo Huang, Linjing Shu
BMC Medical Education.2026;[Epub] CrossRef
Faculty development and institutional readiness for incorporating AI in health professions education- A narrative review
Prathibha Prasad, Mahinour Amin, Elizabeth Fitriana Sari, Lovely M. Annamma, Jayaraj Narayanan, Dinesh Yasothkumar, Mehzabin Ahmed
Frontiers in Dental Medicine.2026;[Epub] CrossRef
The temporal changes in GPT-4 performance on UKMLA practice questions: educational and clinical implications
Ravanth Baskaran, Sai Sirikonda, Aditya Singh, Sripradha Srinivasan, Becky Leveridge, Octavi Casals-Farre, Harmeena Kaur, Susruta Manivannan, Kabilan Elangovan, Chrystie Wan Ning Quek, Kanae Fukutsu, Judith Cave, Bhaskar Kumar Somani, Daniel Shu Wei Ting,
Frontiers in Medicine.2026;[Epub] CrossRef
Artificial intelligence teaching assistants: a scalable solution for supporting struggling medical students
Alina Sami, Mark Adkins, Anne McLeod, Fok-Han Leung, Chris Gilchrist
Academic Medicine.2026;[Epub] CrossRef
ARE AI MODELS READY FOR CLINICAL DECISION-MAKING IN DENTAL BLEACHING? AN EVIDENCE-BASED EVALUATION OF ACCURACY, COMPLETENESS, AND READABILITY OF CHATGPT-5, GEMINI 2.5 PRO, DEEPSEEK V3.2, AND CLAUDE SONNET 4.5
Suzan Cangül, Özkan Adıgüzel, Tuba Tunç, Makbule Taşyürek, Hatice Ortaç
Acta Medica Nicomedia.2026; 9: 1. CrossRef
Artificial Intelligence and our journal, "Einstein São Paulo"
Jacyr Pasternak
einstein (São Paulo).2026;[Epub] CrossRef
Exploring communication self-efficacy and artificial intelligence generated assessment tools in primary care education
Constanze Dietzsch, Johanna Klutmann, Aline Köhler, Sara Volz-Willems, Johannes Jäger, Fabian Dupont
Discover Education.2026;[Epub] CrossRef
Hematology-Oncology Fellows’ Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey
Ariela L. Marshall, Richard C. Godby, Marc Braunstein, Soo Young Kim, Scott Moerdler, Layla Van Doren, Juan Jose Chango Azanza, Ronak Mistry
Journal of Cancer Education.2026;[Epub] CrossRef
AI-assisted patient education: Challenges and solutions in pediatric kidney transplantation
MZ Ihsan, Dony Apriatama, Pithriani, Riza Amalia
Patient Education and Counseling.2025; 131: 108575. CrossRef
Exploring predictors of AI chatbot usage intensity among students: Within- and between-person relationships based on the technology acceptance model
Anne-Kathrin Kleine, Insa Schaffernak, Eva Lermer
Computers in Human Behavior: Artificial Humans.2025; 3: 100113. CrossRef
Artificial intelligence in medical problem-based learning: opportunities and challenges
Yaoxing Chen, Hong Qi, Yu Qiu, Juan Li, Liang Zhu, Xiaoling Gao, Hao Wang, Gan Jiang
Global Medical Education.2025; 2(1): 5. CrossRef
AI-powered standardised patients: evaluating ChatGPT-4o’s impact on clinical case management in intern physicians
Selcen Öncü, Fulya Torun, Hilal Hatice Ülkü
BMC Medical Education.2025;[Epub] CrossRef
UsmleGPT: An AI application for developing MCQs via multi-agent system
Zhehan Jiang, Shicong Feng
Software Impacts.2025; 23: 100742. CrossRef
ChatGPT’s Performance on Portuguese Medical Examination Questions: Comparative Analysis of ChatGPT-3.5 Turbo and ChatGPT-4o Mini
Filipe Prazeres
JMIR Medical Education.2025; 11: e65108. CrossRef
Transforming medical education: leveraging large language models to enhance PBL—a proof-of-concept study
Shoukat Ali Arain, Shahid Akhtar Akhund, Muhammad Abrar Barakzai, Sultan Ayoub Meo
Advances in Physiology Education.2025; 49(2): 398. CrossRef
Integrating artificial intelligence into pre-clinical medical education: challenges, opportunities, and recommendations
Birgit Pohn, Lars Mehnen, Sebastian Fitzek, Kyung-Eun (Anna) Choi, Ralf J. Braun, Sepideh Hatamikia
Frontiers in Education.2025;[Epub] CrossRef
Evaluating the Accuracy and Reliability of Large Language Models (ChatGPT, Claude, DeepSeek, Gemini, Grok, and Le Chat) in Answering Item-Analyzed Multiple-Choice Questions on Blood Physiology
Mayank Agarwal, Priyanka Sharma, Pinaki Wani
Cureus.2025;[Epub] CrossRef
Artificial intelligence-assisted academic writing: recommendations for ethical use
Adam Cheng, Aaron Calhoun, Gabriel Reedy
Advances in Simulation.2025;[Epub] CrossRef
University Educators Perspectives on ChatGPT: A Technology Acceptance Model-Based Study
Muna Barakat, Nesreen A. Salim, Malik Sallam
Open Praxis.2025; 17(1): 129. CrossRef
Knowledge and use, perceptions of benefits and limitations of artificial intelligence chatbots among Italian physiotherapy students: a cross-sectional national study
Fabio Tortella, Alvisa Palese, Andrea Turolla, Greta Castellini, Paolo Pillastrini, Maria Gabriella Landuzzi, Chad Cook, Giovanni Galeoto, Giuseppe Giovannico, Lia Rodeghiero, Silvia Gianola, Giacomo Rossettini
BMC Medical Education.2025;[Epub] CrossRef
Digital and Intelligence Education in Medicine: A Bibliometric and Visualization Analysis Using CiteSpace and VOSviewer
Bing Xiang Yang, FuLing Zhou, Nan Bai, Sichen Zhou, Chunyan Luo, Qing Wang, Arkers Kwan Ching Wong, Frances Lin
Frontiers of Digital Education.2025;[Epub] CrossRef
Prompts, privacy, and personalized learning: integrating AI into nursing education—a qualitative study
Mingyan Shen, Yanping Shen, Fangchi Liu, Jiawen Jin
BMC Nursing.2025;[Epub] CrossRef
The role of ChatGPT-4o in differential diagnosis and management of vertigo-related disorders
Xu Liu, Suming Shi, Xin Zhang, Qianwen Gao, Wuqing Wang
Scientific Reports.2025;[Epub] CrossRef
Situating governance and regulatory concerns for generative artificial intelligence and large language models in medical education
Michael Tran, Chinthaka Balasooriya, Jitendra Jonnagaddala, Gilberto Ka-Kit Leung, Neeraj Mahboobani, Subha Ramani, Joel Rhee, Lambert Schuwirth, Neysan Sedaghat Najafzadeh-Tabrizi, Carolyn Semmler, Zoie SY Wong
npj Digital Medicine.2025;[Epub] CrossRef
Real-world implementation of an AI learning tool-MetaGP-Edu in medical education: A multi-center cohort study
Yili Sun, Fei Liu
Computers & Education.2025; 237: 105388. CrossRef
A Comparative Analysis of the Accuracy and Readability of Popular Artificial Intelligence-Chat Bots for Inguinal Hernia Management
Thisun Udagedara, Ashley Tran, Sumaya Bokhari, Sharon Shiraga, Stuart Abel, Caitlin Houghton, Katie Galvin, Kamran Samakar, Luke R. Putnam
The American Surgeon™.2025; 91(10): 1729. CrossRef
Exploring the Boundaries of AI: ChatGPT’s Accuracy in Anatomical Image Generation & Bone Identification of the Foot
Simran Shamith, Neshal K. Kothari, Serena K. Kothari, Carolyn Giordano
Journal of Orthopaedic Experience & Innovation.2025;[Epub] CrossRef
Intelligent system for foreign students preparation for postgraduate exams in surgery
A. A. Litvin, V. V. Bereshchenko, S. A. Anashkina, A. M. Karamyshau, V. S. Ivanov
Health and Ecology Issues.2025; 22(2): 140. CrossRef
Generative AI in higher education: A cross-sector analysis of ChatGPT's impact on STEM, social sciences, and healthcare
Rouba Jamal Eddine, Ergun Gide, Abdallah Al-Sabbagh
STEM Education.2025; 5(5): 757. CrossRef
Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial
Zhen Wang, Ting-Ting Fan, Meng-Li Li, Nin-Jun Zhu, Xiao-Chen Wang
BMC Medical Education.2025;[Epub] CrossRef
Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review
Isaac Amankwaa, Emmanuel Ekpor, Daniel Cudjoe, Emmanuel Kobiah, Abdul-Karim Jebuni Fuseini, Maximous Diebieri, Sabastin Gyamfi, Sharon Brownie
Nurse Education Today.2025; : 106822. CrossRef
Comparison of the Performance of Five Generative Artificial Intelligence Models on a Medical Molecular Biology Examination
Xiaoying Jiang
Cureus.2025;[Epub] CrossRef
ChatGPT and AI Chatbots in Education: An Umbrella Review of Systematic Reviews, Scoping Reviews, and Meta-Analyses
Emmanouil D. Milakis, Constantina Corazon Argyrakou, Alexandros Melidis, John Vrettaros
International Journal of Education and Information Technologies.2025; 19: 100. CrossRef
Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study
Zongren Ding, Jianxing Zeng, Guoxu Fang, Pengfei Guo, Weiping Zhou, Yongyi Zeng
Scientific Reports.2025;[Epub] CrossRef
A Comparative Study of ChatGPT-4o and DeepSeek Responses to Mandibular Angle Osteotomy Questions
Chenshan Jiang, Wenjie Cheng, Xinyi Jiang, Jianlin Zhang, Xiaojun Tang
Journal of Craniofacial Surgery.2025; 36(7): e1113. CrossRef
AI‐scending the scope: Perspectives on the integration and utilization of artificial intelligence and machine learning in genetic counseling graduate programs
Ofir Feuer, Kyla Holmes, Sarah Kane, Kathryn M. Curry, Daria Ma, Chloe A. Chatwin, Marie Chuldzhyan, Emily Quinn, Nicholas Gorman
Journal of Genetic Counseling.2025;[Epub] CrossRef
How generative artificial intelligence transforms teaching and influences student wellbeing in future education
Marcin Jukiewicz
Frontiers in Education.2025;[Epub] CrossRef
Application and Development of Large Language Models in Smart Inhalers
Wenxu Guo, Zhihong Cheng, Jian Wang
Pharmaceutical Fronts.2025; 07(03): e158. CrossRef
Comparing Large Language Models as Health Literacy Tools: Evaluating and Simplifying Texts on gender-Affirming Surgery
VICTORIA N. Yi, Angel P. Scialdone, Ann Marie Flusche, Kendall Reitz, Holly C. Lewis, William M. Tian, Elda Fisher, Kristen Rezak, Ash Patel
Journal of Health Communication.2025; 30(10-12): 296. CrossRef
Large Language Models in Lung Cancer: A Systematic Review (Preprint)
Ruikang Zhong, Siyi Chen, Zexing Li, Tangke Gao, Yisha Su, Wenzheng Zhang, Dianna Liu, Lei Gao, Kaiwen Hu
Journal of Medical Internet Research.2025;[Epub] CrossRef
Comparative evaluation of large language models performance in medical education using urinary system histology assessment
Anikó Szabó, Ghasem Dolatkhah Laein
Scientific Reports.2025;[Epub] CrossRef
Exploring the nexus of academic integrity and artificial intelligence in higher education: a bibliometric analysis
Daniela Avello, Samuel Aranguren Zurita
International Journal for Educational Integrity.2025;[Epub] CrossRef
AI-powered platform revolutionizing blood cell morphology education for medical students
Xuekai Liu, Lei Shang, Chen Liu, Yongpei Yu, Donghua Shao, Meilin He, Guowei Liang
BMC Medical Education.2025;[Epub] CrossRef
The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students
You Yuan, Jing Fu, Lanlan Leng, Zhuosi Wen, Xiaoman Wei, Die Han, Xinyang Hu, Yu Liang, Qian Luo, Xia Zhang, Rujun Hu
Frontiers in Public Health.2025;[Epub] CrossRef
ChatGPT’s progress over time: A longitudinal enhancing biostatistical problem-solving in medical education
Aleksandra Ignjatović, Marija Anđelković Apostolović, Lazar Stevanović, Pavle Radovanović, Marija Topalović, Tamara Filipović, Suzana Otašević
Health Informatics Journal.2025;[Epub] CrossRef
Applications of artificial intelligence in healthcare simulation: a model of thinking
Adam Cheng, Carolyn McGregor
Advances in Simulation.2025;[Epub] CrossRef
The current status, knowledge, attitudes, and challenges of generative artificial intelligence use among undergraduate nursing students: a single-center cross-sectional survey of western China
Yuanyuan Zhao, You Yuan, Zhuosi Wen, Lanlan Leng, Lei Shi, Xinyang Hu, Xiaoman Wei, Meng Zuo, Jianghong Mou, Qian Luo, Mei Chen, Rujun Hu, Huiming Gao
Frontiers in Public Health.2025;[Epub] CrossRef
Evaluating large language models for mild cognitive impairment among older adults: A bilingual comparison of ChatGPT, Gemini, and Kimi
Yexuan Xiao, Qianhui Pan, Haoyuan Liu, Yilin He, Yuhe Zhang, Nan Jiang
Health Informatics Journal.2025;[Epub] CrossRef
ChatGPT Utility in Medical Education: A Systematic Review and Meta-Analysis
Fariba Hosseinzadegan, Seyedeh Masumeh Hashemi, Seyed Vahid Sharifi, Mehrnoosh Khoshnoodifar
Health Science Monitor.2025; 4(3): 173. CrossRef
A systematic review of the impact of generative AI on postgraduate research: opportunities, challenges, and ethical implications
Vicent Mabirizi, Calorine Katushabe, Gloria Muhoza, Jack Rugasira
Discover Artificial Intelligence.2025;[Epub] CrossRef
Evaluating Science Readiness of Pre-Service Elementary Teachers Through Diagnostic Assessment and Parental Feedback: Implications for Teacher Education
Zydrick L Avelino
Integrated Science Education Journal.2025; 6(3): 185. CrossRef
How can artificial intelligence transform the training of medical students and physicians?
Yilin Ning, Jasmine Chiat Ling Ong, Haoran Cheng, Haibo Wang, Daniel Shu Wei Ting, Yih Chung Tham, Tien Yin Wong, Nan Liu
The Lancet Digital Health.2025; 7(10): 100900. CrossRef
Poor Performance of Large Language Models Based on the Diabetes and Endocrinology Specialty Certificate Examination of the United Kingdom
Ka Siu Fan, Jeffrey Gan, Isabelle X Zou, Maja Kaladjiska, Monique B Inguanez, Gillian L Garden
Cureus.2025;[Epub] CrossRef
Perceptions and Use of Generative Artificial Intelligence in Medical Students: A Multicenter Survey
Cecilia Tran, Brett N. Hryciw, Sean William Moore, Alan Chaput, Andrew John Ervine Seely
Journal of Medical Education and Curricular Development.2025;[Epub] CrossRef
Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study
Busra Nur Gokkurt Yilmaz, Furkan Ozbey, Birkan Eyup Yilmaz
BMC Medical Education.2025;[Epub] CrossRef
Academic misconduct and artificial intelligence use by medical students, interns and PhD students in Ukraine: a cross-sectional study
Lesya Lymar, Iurii Kuchyn, Kateryna Bielka, Livia Puljak
BMC Medical Education.2025;[Epub] CrossRef
Medical multimodal large language models: A systematic review
Yuan Hu, Chenhan Xu, Bo Lin, Weibin Yang, Yuan Yan Tang
Intelligent Oncology.2025; 1(4): 308. CrossRef
Research on the training strategy of college students' design thinking and innovation ability based on multimodal large model
Qing Liu, Wei Xue, Lingbo Meng, Yilin Zhu, Jixin Li
Frontiers in Education.2025;[Epub] CrossRef
Clinical Assessment of Large Language Models: A Comprehensive Multi-domain Performance Study for Healthcare Applications
Harsh Hirani, Bharat Saboo, Alok Modi, Palak Modi, Shambo Samrat Samajdar, Banshi Saboo, Manoj Chawla, Anuj Maheshwari, Amit Gupta, Rakesh Parikh, S. S. Dariya, Ritu Johari, Rutul Gokhlani, Param Kadam
International Journal of Diabetes and Technology.2025; 4(4): 159. CrossRef
Evaluating the impact of AI-tutoring versus expert human instruction on surgical skills in medical students
Yili Sun, Fei Liu
Education and Information Technologies.2025; 30(18): 26413. CrossRef
AI adoption in higher education: Exploring attitudes and perceived benefits between users and non-users
Nevenka Popović Šević, Aleksandar Šević, Milica Slijepčević, Jelena Krstić
Online Journal of Communication and Media Technologies.2025; 15(4): e202528. CrossRef
Integrating generative Artificial Intelligence into student learning: A systematic review from a TPACK perspective
Xiaofan Liu, Baichang Zhong
Educational Research Review.2025; 49: 100741. CrossRef
Large language models as educational collaborators: developing non-conventional teaching aids in pharmacology & therapeutics
Kannan Sridharan, Gowri Sivaramakrishnan
BMC Medical Education.2025;[Epub] CrossRef
Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review
Junseok Kang, Jihyun Ahn
Ewha Medical Journal.2025; 48(4): e53. CrossRef
A bibliometric analysis of artificial intelligence in medical education (2015–2025)
Wendan Cheng, Zhongyao Hu, Haoran Yu
Medicine.2025; 104(46): e45684. CrossRef
Embracing the Future of Medical Education With Large Language Model–Based Virtual Patients: Scoping Review
Jianwen Zeng, Wenhao Qi, Shiying Shen, Xin Liu, Sixie Li, Bing Wang, Chaoqun Dong, Xiaohong Zhu, Yankai Shi, Xiajing Lou, Bingsheng Wang, Jiani Yao, Guowei Jiang, Qiong Zhang, Shihua Cao
Journal of Medical Internet Research.2025; 27: e79091. CrossRef
Artificial intelligence in undergraduate medical education: an updated scoping review
Jennifer Simoni, Judith Urtubia-Fernandez, Elisa Mengual, Diglio A. Simoni, Montserrat Royo, Diego Egaña-Yin, Oliver L. A. Hertog, Lourdes López-Ortiz, Adrián Muñoz-Tomás, Paula Santiago-Martínez, Adrián Vahamaki, José Luis Pereira
BMC Medical Education.2025;[Epub] CrossRef
Considerations for Patient Privacy of Large Language Models in Health Care: Scoping Review
Xiaoying Zhong, Siyi Li, Zhao Chen, Long Ge, Dongdong Yu, Shijia Wang, Liangzhen You, Hongcai Shang
Journal of Medical Internet Research.2025; 27: e76571. CrossRef
The Growing Importance of Soft Skills in Medical Education in the AI Era: Balancing Humanistic Care and Artificial Intelligence
Effie Simou
International Medical Education.2025; 4(4): 50. CrossRef
Integrating Generative AI in Health Education: A Scoping Review and Implementation Framework
Kellie Toohey, Zach Quince, Felicity Walker, Linda Furness, Michelle Bissett, Carlie Daley, Kachina Allen, Natalie Munro, Andy Smidt, Jodie Cochrane Wilkie, Louise Horstmanshof, Kathryn Baltrotsky, Fiona Naumann
Medical Science Educator.2025; 35(6): 2751. CrossRef
Beyond technical efficacy: challenges and critical concerns of large language model’s impact on medical education in China: a systematic review
Puwen Shen, Yongxiang Yuan, Xinyao He, Fang Wang
Global Medical Education.2025; 2(1): 113. CrossRef
The Application of Flipped Classroom Integrated with ChatGPT in Improving Graduate Education on Choroidal Melanoma
Shengyu Tan, Qijian Deng, Qiaoyan Wei, Xuan Zhu, Shengguo Li
Journal of Cancer Education.2025;[Epub] CrossRef
Case study: creating an ‘AI for Academic Writing Skills’ induction session for postgraduate life science courses
Jennifer Carter, Anne Ferrey, Hubert Lam, Kelly Webb-Davies, Damion Young, Barbara Zonta, Delia O' Rourke
Emerging Topics in Life Sciences.2025;[Epub] CrossRef
Chatbots in neurology and neuroscience: Interactions with students, patients and neurologists
Stefano Sandrone
Brain Disorders.2024; 15: 100145. CrossRef
ChatGPT in education: unveiling frontiers and future directions through systematic literature review and bibliometric analysis
Buddhini Amarathunga
Asian Education and Development Studies.2024; 13(5): 412. CrossRef
Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination
Ching-Hua Hsieh, Hsiao-Yun Hsieh, Hui-Ping Lin
Heliyon.2024; 10(14): e34851. CrossRef
Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172
Ken Masters, Anne Herrmann-Werner, Teresa Festl-Wietek, David Taylor
Medical Teacher.2024; 46(10): 1258. CrossRef
A Comparative Analysis of ChatGPT and Medical Faculty Graduates in Medical Specialization Exams: Uncovering the Potential of Artificial Intelligence in Medical Education
Gülcan Gencer, Kerem Gencer
Cureus.2024;[Epub] CrossRef
Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review
Sang-Jun Kim
Science Editing.2024; 11(2): 96. CrossRef
Innovation Off the Bat: Bridging the ChatGPT Gap in Digital Competence among English as a Foreign Language Teachers
Gulsara Urazbayeva, Raisa Kussainova, Aikumis Aibergen, Assel Kaliyeva, Gulnur Kantayeva
Education Sciences.2024; 14(9): 946. CrossRef
Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications
Ji Young Chung, Seung-Hoon Jeong
Online Journal of Communication and Media Technologies.2024; 14(4): e202457. CrossRef
Unveiling the bright side and dark side of AI-based ChatGPT : a bibliographic and thematic approach
Chandan Kumar Tiwari, Mohd. Abass Bhat, Abel Dula Wedajo, Shagufta Tariq Khan
Journal of Decision Systems.2024; : 1. CrossRef
Artificial Intelligence in Medical Education and Mentoring in Rehabilitation Medicine
Julie K. Silver, Mustafa Reha Dodurgali, Nara Gavini
American Journal of Physical Medicine & Rehabilitation.2024; 103(11): 1039. CrossRef
The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education
Sauliha Rabia Alli, Soaad Qahhār Hossain, Sunit Das, Ross Upshur
JMIR Medical Education.2024; 10: e51446. CrossRef
A Systematic Literature Review of Empirical Research on Applying Generative Artificial Intelligence in Education
Xin Zhang, Peng Zhang, Yuan Shen, Min Liu, Qiong Wang, Dragan Gašević, Yizhou Fan
Frontiers of Digital Education.2024; 1(3): 223. CrossRef

Research articles

Discovering social learning ecosystems during clinical clerkship from United States medical students’ feedback encounters: a content analysis: Anna Therese Cianciolo, Heeyoung Han, Lydia Anne Howes, Debra Lee Klamen, Sophia Matos; J Educ Eval Health Prof. 2024;21:5. Published online February 28, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.5

4,020 View
309 Download

Abstract PDF Supplementary Material: Purpose
We examined United States medical students’ self-reported feedback encounters during clerkship training to better understand in situ feedback practices. Specifically, we asked: Who do students receive feedback from, about what, when, where, and how do they use it? We explored whether curricular expectations for preceptors’ written commentary aligned with feedback as it occurs naturalistically in the workplace.
Methods
This study occurred from July 2021 to February 2022 at Southern Illinois University School of Medicine. We used qualitative survey-based experience sampling to gather students’ accounts of their feedback encounters in 8 core specialties. We analyzed the who, what, when, where, and why of 267 feedback encounters reported by 11 clerkship students over 30 weeks. Code frequencies were mapped qualitatively to explore patterns in feedback encounters.
Results
Clerkship feedback occurs in patterns apparently related to the nature of clinical work in each specialty. These patterns may be attributable to each specialty’s “social learning ecosystem”—the distinctive learning environment shaped by the social and material aspects of a given specialty’s work, which determine who preceptors are, what students do with preceptors, and what skills or attributes matter enough to preceptors to comment on.
Conclusion
Comprehensive, standardized expectations for written feedback across specialties conflict with the reality of workplace-based learning. Preceptors may be better able—and more motivated—to document student performance that occurs as a natural part of everyday work. Nurturing social learning ecosystems could facilitate workplace-based learning such that, across specialties, students acquire a comprehensive clinical skillset appropriate for graduation.

Effect of a transcultural nursing course on improving the cultural competency of nursing graduate students in Korea: a before-and-after study: Kyung Eui Bae, Geum Hee Jeong; J Educ Eval Health Prof. 2023;20:35. Published online December 4, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.35

7,861 View
348 Download
3 Web of Science
6 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to evaluate the impact of a transcultural nursing course on enhancing the cultural competency of graduate nursing students in Korea. We hypothesized that participants’ cultural competency would significantly improve in areas such as communication, biocultural ecology and family, dietary habits, death rituals, spirituality, equity, and empowerment and intermediation after completing the course. Furthermore, we assessed the participants’ overall satisfaction with the course.
Methods
A before-and-after study was conducted with graduate nursing students at Hallym University, Chuncheon, Korea, from March to June 2023. A transcultural nursing course was developed based on Giger & Haddad’s transcultural nursing model and Purnell’s theoretical model of cultural competence. Data was collected using a cultural competence scale for registered nurses developed by Kim and his colleagues. A total of 18 students participated, and the paired t-test was employed to compare pre-and post-intervention scores.
Results
The study revealed significant improvements in all 7 categories of cultural nursing competence (P<0.01). Specifically, the mean differences in scores (pre–post) ranged from 0.74 to 1.09 across the categories. Additionally, participants expressed high satisfaction with the course, with an average score of 4.72 out of a maximum of 5.0.
Conclusion
The transcultural nursing course effectively enhanced the cultural competency of graduate nursing students. Such courses are imperative to ensure quality care for the increasing multicultural population in Korea.

Citations

Citations to this article as recorded by

A Primer for Applying the Purnell Model: Enhancing Cultural Competence in Diabetes Prevention Programs
Judy Harara, Hannah Sheridan, Adrienne McSwain-Dawson, Michael Stellefson
American Journal of Health Education.2026; 57(2): 145. CrossRef
Reflections of Cultural Differences on Nursing Care: A Systematic Review of International Quantitative Evidence
Hatice Öntürk Akyüz, Hatice Gülsoy
Holistic Nursing Practice.2026;[Epub] CrossRef
Effect of Cultural Communication Strategy in Preventing Child Marriage in a Region with Strong Cultural Resistance A Quasi-Experimental Study
Ayu Dwi Putri Rusman , Muhammad Syafar, Lalu Muhammad Saleh , Stang Stang, Wahiduddin Wahiduddin , Muhammad Alwi Arifin , Anwar Mallongi , Sumarni Marwang
Salud, Ciencia y Tecnología.2025; 5: 1789. CrossRef
Qualitative evaluation of a ChatGPT-integrated transcultural nursing course for a diverse group
Ecem Çi̇çek Gümüş, İlknur Dolu
Nurse Education in Practice.2025; 87: 104491. CrossRef
Factors Influencing Nurses’ Person-Centered Care: A Cross-sectional Descriptive Study
Heesun Jang, Young A Kim
Korean Journal of Adult Nursing.2025; 37(3): 344. CrossRef
Transcultural Care During the Dying Process: A Phenomenological Study of Nurses’ Experiences
Lorena Riutort Durán, Carmen Mena Biosca, Antonia Pades-Jiménez, Xandra Gonzalez-Garcia
INQUIRY: The Journal of Health Care Organization, Provision, and Financing.2025;[Epub] CrossRef

Effect of motion-graphic video-based training on the performance of operating room nurse students in cataract surgery in Iran: a randomized controlled study: Behnaz Fatahi, Samira Fatahi, Sohrab Nosrati, Masood Bagheri; J Educ Eval Health Prof. 2023;20:34. Published online November 28, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.34

7,829 View
2,147,483,844 Download

Abstract PDF Supplementary Material: Purpose
The present study was conducted to determine the effect of motion-graphic video-based training on the performance of operating room nurse students in cataract surgery using phacoemulsification at Kermanshah University of Medical Sciences in Iran.
Methods
This was a randomized controlled study conducted among 36 students training to become operating room nurses. The control group only received routine training, and the intervention group received motion-graphic video-based training on the scrub nurse’s performance in cataract surgery in addition to the educator’s training. The performance of the students in both groups as scrub nurses was measured through a researcher-made checklist in a pre-test and a post-test.
Results
The mean scores for performance in the pre-test and post-test were 17.83 and 26.44 in the control group and 18.33 and 50.94 in the intervention group, respectively, and a significant difference was identified between the mean scores of the pre- and post-test in both groups (P=0.001). The intervention also led to a significant increase in the mean performance score in the intervention group compared to the control group (P=0.001).
Conclusion
Considering the significant difference in the performance score of the intervention group compared to the control group, motion-graphic video-based training had a positive effect on the performance of operating room nurse students, and such training can be used to improve clinical training.

Brief report

ChatGPT (GPT-3.5) as an assistant tool in microbial pathogenesis studies in Sweden: a cross-sectional comparative study: Catharina Hultgren, Annica Lindkvist, Volkan Özenci, Sophie Curbo; J Educ Eval Health Prof. 2023;20:32. Published online November 22, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.32

5,137 View
183 Download
7 Web of Science
7 Crossref

Abstract PDF Supplementary Material

ChatGPT (GPT-3.5) has entered higher education and there is a need to determine how to use it effectively. This descriptive study compared the ability of GPT-3.5 and teachers to answer questions from dental students and construct detailed intended learning outcomes. When analyzed according to a Likert scale, we found that GPT-3.5 answered the questions from dental students in a similar or even more elaborate way compared to the answers that had previously been provided by a teacher. GPT-3.5 was also asked to construct detailed intended learning outcomes for a course in microbial pathogenesis, and when these were analyzed according to a Likert scale they were, to a large degree, found irrelevant. Since students are using GPT-3.5, it is important that instructors learn how to make the best use of it both to be able to advise students and to benefit from its potential.

Citations

Citations to this article as recorded by

Global Trends in the Use of Artificial Intelligence in Dental Education: A Bibliometric Analysis
Margarita Iniesta, Juan José Pérez‐Higueras
European Journal of Dental Education.2026; 30(2): 427. CrossRef
Educational Applications of ChatGPT in University‐Based Dental Education. A Systematic Review
Juan Ignacio Aura‐Tormos, Maria Llacer‐Martinez, Ines Torres‐Osca
European Journal of Dental Education.2026; 30(2): 644. CrossRef
Choosing not to use generative AI in higher education: a mixed methods study of student reasoning, assessment design, and AI literacy
Annica Lindkvist, Sophie Curbo, Catharina Hultgren
Frontiers in Education.2026;[Epub] CrossRef
Unlocking learning: exploring take-home examinations and viva voce examinations in microbiology education for biomedical laboratory science students
Sophie Curbo, Annica Lindkvist, Catharina Hultgren, Jorge Cervantes
Journal of Microbiology & Biology Education.2025;[Epub] CrossRef
How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
Yoonhwan Lee, Sun Huh
Endocrinology and Metabolism.2025; 40(5): 659. CrossRef
Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef
Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soobin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef

Research articles

Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study: Janghee Park; J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.29

9,688 View
326 Download
19 Web of Science
21 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to analyze patterns of using ChatGPT before and after group activities and to explore medical students’ perceptions of ChatGPT as a feedback tool in the classroom.
Methods
The study included 99 2nd-year pre-medical students who participated in a “Leadership and Communication” course from March to June 2023. Students engaged in both individual and group activities related to negotiation strategies. ChatGPT was used to provide feedback on their solutions. A survey was administered to assess students’ perceptions of ChatGPT’s feedback, its use in the classroom, and the strengths and challenges of ChatGPT from May 17 to 19, 2023.
Results
The students responded by indicating that ChatGPT’s feedback was helpful, and revised and resubmitted their group answers in various ways after receiving feedback. The majority of respondents expressed agreement with the use of ChatGPT during class. The most common response concerning the appropriate context of using ChatGPT’s feedback was “after the first round of discussion, for revisions.” There was a significant difference in satisfaction with ChatGPT’s feedback, including correctness, usefulness, and ethics, depending on whether or not ChatGPT was used during class, but there was no significant difference according to gender or whether students had previous experience with ChatGPT. The strongest advantages were “providing answers to questions” and “summarizing information,” and the worst disadvantage was “producing information without supporting evidence.”
Conclusion
The students were aware of the advantages and disadvantages of ChatGPT, and they had a positive attitude toward using ChatGPT in the classroom.

Citations

Citations to this article as recorded by

An Alternative Approach in Anatomy Education: Design of a Learning Environment Based on Artificial Intelligence‐Supported Virtual Manipulatives and Investigation of Its Effectiveness
Gunes Bolatli, Salih Birisci, Zafer Bolatli
Clinical Anatomy.2026; 39(1): 30. CrossRef
Applications and Outcomes of Large‑Language‑Model‑Generated Feedback in Undergraduate Medical Education: A Scoping Review
Yavuz Selim Kıyak, Tuğba İş-Kara, Emre Emekli
Medical Science Educator.2026; 36(1): 81. CrossRef
Attitudes and perceptions of the application of large language models among health professionals: A mixed-methods systematic review
Wen Luo, Tao Feng, Ting Zhang, Xinyu Chen, Xianying Lu, Yuhang Li, Chaoming Hou, Jing Gao
Public Health.2026; 254: 106252. CrossRef
Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis
Nora Arvai, Bertalan Meskó, Gellért Katonai
JMIR Medical Education.2026; 12: e85373. CrossRef
Mapping the landscape of AI-driven feedback in education: a scoping review
Anastasiya A. Lipnevich, Carmen A. Taranto, Christopher DeLuca, Ephraim Nukpetsi, Nathan Rickey, Therese Hopfenbeck, Emma Carter, Joshua McGrane
Frontiers in Education.2026;[Epub] CrossRef
The Impact of ChatGPT on Higher Education: A Systematic Review of Global Opportunities, Perceptions, and Challenges
Olukayode Emmanuel Apata, Oi‐Man Kwok, Segun Timothy Ajose
Journal of Computer Assisted Learning.2026;[Epub] CrossRef
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Dejan Ravšelj, Damijana Keržič, Nina Tomaževič, Lan Umek, Nejc Brezovar, Noorminshah A. Iahad, Ali Abdulla Abdulla, Anait Akopyan, Magdalena Waleska Aldana Segura, Jehan AlHumaid, Mohamed Farouk Allam, Maria Alló, Raphael Papa Kweku Andoh, Octavian Andron
PLOS ONE.2025; 20(2): e0315011. CrossRef
Generative AI in Otolaryngology Residency Personal Statement Writing: A Mixed‐Methods Analysis
Jacob G. J. Wihlidal, Nikolaus E. Wolter, Evan J. Propst, Vincent Lin, Michael Au, Shaunak Amin, Jennifer M. Siu
The Laryngoscope.2025; 135(10): 3570. CrossRef
Feasibility of a Randomized Controlled Trial of Large AI-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students: Pilot Randomized Parallel-Group Study
Raúl Ferrer-Peña, Silvia Di-Bonaventura, Alberto Pérez-González, Alfredo Lerín-Calvo
JMIR Formative Research.2025; 9: e66126. CrossRef
Applications of Artificial Intelligence for Nonpsychomotor Skills Training in Health Professions Education: A Scoping Review
Kenya A Costa-Dookhan, Zachary Adirim, Marta Maslej, Kayle Donner, Terri Rodak, Sophie Soklaridis, Sanjeev Sockalingam, Anupam Thakur
Academic Medicine.2025; 100(5): 635. CrossRef
MD Student Perceptions of ChatGPT for Reflective Writing Feedback in Undergraduate Medical Education
Nabil Haider, Leo Morjaria, Urmi Sheth, Nujud Al-Jabouri, Matthew Sibbald
International Medical Education.2025; 4(3): 27. CrossRef
Exploring medical students’ attitudes and perceptions toward artificial intelligence in medicine in Shandong Province, China
Mingchan Liu, Yi Cheng, Shu Li, Shanshan Wang, Feng Du, Xiaonan Wei, Zhiying Ai, Siyuan Yan
BMC Medical Education.2025;[Epub] CrossRef
How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
Yoonhwan Lee, Sun Huh
Endocrinology and Metabolism.2025; 40(5): 659. CrossRef
Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef
Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students
Yijun Wu, Yue Zheng, Baijie Feng, Yuqi Yang, Kai Kang, Ailin Zhao
JMIR Medical Education.2024; 10: e52483. CrossRef
Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students’ Perception, and Applications
Anita V Thomae, Claudia M Witt, Jürgen Barth
JMIR Medical Education.2024; 10: e50545. CrossRef
A cross sectional investigation of ChatGPT-like large language models application among medical students in China
Guixia Pan, Jing Ni
BMC Medical Education.2024;[Epub] CrossRef
A Pilot Study of Medical Student Opinions on Large Language Models
Alan Y Xu, Vincent S Piranio, Skye Speakman, Chelsea D Rosen, Sally Lu, Chris Lamprecht, Robert E Medina, Maisha Corrielus, Ian T Griffin, Corinne E Chatham, Nicolas J Abchee, Daniel Stribling, Phuong B Huynh, Heather Harrell, Benjamin Shickel, Meghan Bre
Cureus.2024;[Epub] CrossRef
The intent of ChatGPT usage and its robustness in medical proficiency exams: a systematic review
Tatiana Chaiban, Zeinab Nahle, Ghaith Assi, Michelle Cherfane
Discover Education.2024;[Epub] CrossRef
ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students
Mohammed Zawiah, Fahmi Al-Ashwal, Lobna Gharaibeh, Rana Abu Farha, Karem Alzoubi, Khawla Abu Hammour, Qutaiba A Qasim, Fahd Abrah
Journal of Multidisciplinary Healthcare.2023; Volume 16: 4099. CrossRef
Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soobin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef

Development and validation of the student ratings in clinical teaching scale in Australia: a methodological study: Pin-Hsiang Huang, Anthony John O’Sullivan, Boaz Shulruf; J Educ Eval Health Prof. 2023;20:26. Published online September 5, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.26

4,918 View
204 Download
1 Web of Science

Abstract PDF Supplementary Material: Purpose
This study aimed to devise a valid measurement for assessing clinical students’ perceptions of teaching practices.
Methods
A new tool was developed based on a meta-analysis encompassing effective clinical teaching-learning factors. Seventy-nine items were generated using a frequency (never to always) scale. The tool was applied to the University of New South Wales year 2, 3, and 6 medical students. Exploratory and confirmatory factor analysis (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA], respectively) were conducted to establish the tool’s construct validity and goodness of fit, and Cronbach’s α was used for reliability.
Results
In total, 352 students (44.2%) completed the questionnaire. The EFA identified student-centered learning, problem-solving learning, self-directed learning, and visual technology (reliability, 0.77 to 0.89). CFA showed acceptable goodness of fit (chi-square P<0.01, comparative fit index=0.930 and Tucker-Lewis index=0.917, root mean square error of approximation=0.069, standardized root mean square residual=0.06).
Conclusion
The established tool—Student Ratings in Clinical Teaching (STRICT)—is a valid and reliable tool that demonstrates how students perceive clinical teaching efficacy. STRICT measures the frequency of teaching practices to mitigate the biases of acquiescence and social desirability. Clinical teachers may use the tool to adapt their teaching practices with more active learning activities and to utilize visual technology to facilitate clinical learning efficacy. Clinical educators may apply STRICT to assess how these teaching practices are implemented in current clinical settings.

Experience of introducing an electronic health records station in an objective structured clinical examination to evaluate medical students’ communication skills in Canada: a descriptive study: Kuan-chin Jean Chen, Ilona Bartman, Debra Pugh, David Topps, Isabelle Desjardins, Melissa Forgie, Douglas Archibald; J Educ Eval Health Prof. 2023;20:22. Published online July 4, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.22

7,475 View
191 Download
2 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
There is limited literature related to the assessment of electronic medical record (EMR)-related competencies. To address this gap, this study explored the feasibility of an EMR objective structured clinical examination (OSCE) station to evaluate medical students’ communication skills by psychometric analyses and standardized patients’ (SPs) perspectives on EMR use in an OSCE.
Methods
An OSCE station that incorporated the use of an EMR was developed and pilot-tested in March 2020. Students’ communication skills were assessed by SPs and physician examiners. Students’ scores were compared between the EMR station and 9 other stations. A psychometric analysis, including item total correlation, was done. SPs participated in a post-OSCE focus group to discuss their perception of EMRs’ effect on communication.
Results
Ninety-nine 3rd-year medical students participated in a 10-station OSCE that included the use of the EMR station. The EMR station had an acceptable item total correlation (0.217). Students who leveraged graphical displays in counseling received higher OSCE station scores from the SPs (P=0.041). The thematic analysis of SPs’ perceptions of students’ EMR use from the focus group revealed the following domains of themes: technology, communication, case design, ownership of health information, and timing of EMR usage.
Conclusion
This study demonstrated the feasibility of incorporating EMR in assessing learner communication skills in an OSCE. The EMR station had acceptable psychometric characteristics. Some medical students were able to efficiently use the EMRs as an aid in patient counseling. Teaching students how to be patient-centered even in the presence of technology may promote engagement.

Citations

Citations to this article as recorded by

Medical students’ perspectives on the role of OSPE and OSCE in the educational journey and contribution to career development: A cross-sectional study
Fahad Abdulaziz Alrashed, Tauseef Ahmad, Abdulrahman M. Alsubiheen, Saad A. Alhammad, Mishal M. Aldaihan, Alaa M. Albishi, Zafrul Hasan
Medicine.2026; 105(3): e47233. CrossRef
Usage and perception of electronic medical records (EMR) among medical students in southwestern Nigeria
A. A. Adeyeye, A. O. Ajose, O. M. Oduola, B. A. Akodu, A. Olufadeji
Discover Public Health.2024;[Epub] CrossRef

Factors influencing the learning transfer of nursing students in a non-face-to-face educational environment during the COVID-19 pandemic in Korea: a cross-sectional study using structural equation modeling: Geun Myun Kim, Yunsoo Kim, Seong Kwang Kim; J Educ Eval Health Prof. 2023;20:14. Published online April 27, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.14

5,726 View
205 Download
3 Web of Science
5 Crossref

Abstract PDF Supplementary Material

Purpose
The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning.
Methods
In this cross-sectional study, data were collected via online surveys from February 9 to March 1, 2022, from 218 nursing students in Korea. Learning transfer, learning immersion, learning satisfaction, learning efficacy, self-directed learning ability and information technology utilization ability were analyzed using IBM SPSS for Windows ver. 22.0 and AMOS ver. 22.0.
Results
The assessment of structural equation modeling showed adequate model fit, with normed χ2=1.74 (P<0.024), goodness-of-fit index=0.97, adjusted goodness-of-fit index=0.93, comparative fit index=0.98, root mean square residual=0.02, Tucker-Lewis index=0.97, normed fit index=0.96, and root mean square error of approximation=0.06. In a hypothetical model analysis, 9 out of 11 pathways of the hypothetical structural model for learning transfer in nursing students were statistically significant. Learning self-efficacy and learning immersion of nursing students directly affected learning transfer, and subjective information technology utilization ability, self-directed learning ability, and learning satisfaction were variables with indirect effects. The explanatory power of immersion, satisfaction, and self-efficacy for learning transfer was 44.4%.
Conclusion
The assessment of structural equation modeling indicated an acceptable fit. It is necessary to improve the transfer of learning through the development of a self-directed program for learning ability improvement, including the use of information technology in nursing students’ learning environment in non-face-to-face conditions.

Citations

Citations to this article as recorded by

Flow in Relation to Academic Achievement in Online-Learning: A Meta-Analysis Study
Da Xing, Yunjung Lee, Gyun Heo
Measurement: Interdisciplinary Research and Perspectives.2025; 23(1): 1. CrossRef
Mediating Effects of Learning Environment Adaptation and Self-Efficacy on College Students’ Self-Directed Learning
Yilin Ren, Jienan Wu
Applied Mathematics and Nonlinear Sciences.2025;[Epub] CrossRef
The Impact of the COVID-19 Pandemic on Clinical Practice Education in South Korea: A Cross-Sectional Study
Kuemju Park
International Journal of Environmental Research and Public Health.2025; 22(4): 577. CrossRef
The Mediating Effect of Perceived Institutional Support on Inclusive Leadership and Academic Loyalty in Higher Education
Olabode Gbobaniyi, Shalini Srivastava, Abiodun Kolawole Oyetunji, Chiemela Victor Amaechi, Salmia Binti Beddu, Bajpai Ankita
Sustainability.2023; 15(17): 13195. CrossRef
Transfer of Learning of New Nursing Professionals: Exploring Patterns and the Effect of Previous Work Experience
Helena Roig-Ester, Paulina Elizabeth Robalino Guerra, Carla Quesada-Pallarès, Andreas Gegenfurtner
Education Sciences.2023; 14(1): 52. CrossRef

Students’ performance of and perspective on an objective structured practical examination for the assessment of preclinical and practical skills in biomedical laboratory science students in Sweden: a 5-year longitudinal study: Catharina Hultgren, Annica Lindkvist, Sophie Curbo, Maura Heverin; J Educ Eval Health Prof. 2023;20:13. Published online April 6, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.13

5,943 View
182 Download
3 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
It aims to find students’ performance of and perspectives on an objective structured practical examination (OSPE) for assessment of laboratory and preclinical skills in biomedical laboratory science (BLS). It also aims to investigate the perception, acceptability, and usefulness of OSPE from the students’ and examiners’ point of view.
Methods
This was a longitudinal study to implement an OSPE in BLS. The student group consisted of 198 BLS students enrolled in semester 4, 2015–2019 at Karolinska University Hospital Huddinge, Sweden. Fourteen teachers evaluated the performance by completing a checklist and global rating scales. A student survey questionnaire was administered to the participants to evaluate the student perspective. To assess quality, 4 independent observers were included to monitor the examiners.
Results
Almost 50% of the students passed the initial OSPE. During the repeat OSPE, 73% of the students passed the OSPE. There was a statistically significant difference between the first and the second repeat OSPE (P<0.01) but not between the first and the third attempt (P=0.09). The student survey questionnaire was completed by 99 of the 198 students (50%) and only 63 students responded to the free-text questions (32%). According to these responses, some stations were perceived as more difficult, albeit they considered the assessment to be valid. The observers found the assessment protocols and examiner’s instructions assured the objectivity of the examination.
Conclusion
The introduction of an OSPE in the education of biomedical laboratory scientists was a reliable, and useful examination of practical skills.

Citations

Citations to this article as recorded by

Choosing not to use generative AI in higher education: a mixed methods study of student reasoning, assessment design, and AI literacy
Annica Lindkvist, Sophie Curbo, Catharina Hultgren
Frontiers in Education.2026;[Epub] CrossRef
Unlocking learning: exploring take-home examinations and viva voce examinations in microbiology education for biomedical laboratory science students
Sophie Curbo, Annica Lindkvist, Catharina Hultgren, Jorge Cervantes
Journal of Microbiology & Biology Education.2025;[Epub] CrossRef

Comparison of nursing students’ performance of cardiopulmonary resuscitation between 1 semester and 3 semesters of manikin simulations in the Czech Republic: a non-randomized controlled study: Vera Spatenkova, Iveta Zvercova, Zdenek Jindrisek, Ivana Veverkova, Eduard Kuriscak; J Educ Eval Health Prof. 2023;20:9. Published online March 31, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.9

5,241 View
149 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to assess the effect of simulation teaching in critical care courses in a nursing study program on the quality of chest compressions of cardiopulmonary resuscitation (CPR).
Methods
An observational cross-sectional study was conducted at the Faculty of Health Studies at the Technical University of Liberec. The success rate of CPR was tested in exams comparing 2 groups of students, totaling 66 different individuals, who completed half a year (group 1: intermediate exam with model simulation) or 1.5 years (group 2: final theoretical critical care exam with model simulation) of undergraduate nursing critical care education taught completely with a Laerdal SimMan 3G simulator. The quality of CPR was evaluated according to 4 components: compression depth, compression rate, time of correct frequency, and time of correct chest release.
Results
Compression depth was significantly higher in group 2 than in group 1 (P=0.016). There were no significant differences in the compression rate (P=0.210), time of correct frequency (P=0.586), or time of correct chest release (P=0.514).
Conclusion
Nursing students who completed the final critical care exam showed an improvement in compression depth during CPR after 2 additional semesters of critical care teaching compared to those who completed the intermediate exam. The above results indicate that regularly scheduled CPR training is necessary during critical care education for nursing students.

Citations

Citations to this article as recorded by

Evaluation of Learning Outcomes Among Practical Nursing Students After Using Three-Dimensional Technology in Their Studies
Mika Alhonkoski, Marjaana Veermans, Hanna Nurmi, Sari Kotonen, Camilla Strandell-Laine, Kalle Artukka, Leena Salminen
CIN: Computers, Informatics, Nursing.2025;[Epub] CrossRef

Effect of a smartphone-based online electronic logbook to evaluate the clinical skills of nurse anesthesia students in Iran: a randomized controlled study: Ali Khalafi, Nahid Jamshidi, Nasrin Khajeali, Saeed Ghanbari; J Educ Eval Health Prof. 2023;20:10. Published online March 31, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.10

5,732 View
189 Download
1 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
This study was conducted to evaluate a smartphone-based online electronic logbook used to assess the clinical skills of nurse anesthesia students in Iran.
Methods
This randomized controlled study was conducted after tool development at Ahvaz Jundishapur University of Medical Sciences in Ahvaz, Iran from January 2022 to December 2022. The online electronic logbook involved in this study was an Android-compatible application used to evaluate the clinical skills of nurse anesthesia students. In the implementation phase, the online electronic logbook was piloted for 3 months in anesthesia training in comparison with a paper logbook. For this purpose, 49 second- and third-year anesthesia nursing students selected using the census method were assigned to intervention (online electronic logbook) and control (paper logbook) groups. The online electronic logbook and paper logbook were compared in terms of student satisfaction and learning outcomes.
Results
A total of 39 students participated in the study. The mean satisfaction score of the intervention group was significantly higher than that of the control group (P=0.027). The mean score of learning outcomes was also significantly higher for the intervention than the control group (P=0.028).
Conclusion
Smartphone technology can provide a platform for improving the evaluation of the clinical skills of nursing anesthesia students, leading to increased satisfaction and improved learning outcomes.

Citations

Citations to this article as recorded by

Structure and Utility of e‐Portfolios for Reflection and Learning in Healthcare Professional Education: A Narrative Review
Montini Claudia, Mellucci Claudia, Galletti Caterina
The Clinical Teacher.2026;[Epub] CrossRef
Investigating the Current Status of the Clinical Internship Curriculum in Paramedical Student Training Courses: A Grounded Theory Approach
Saeid Rahmati Kahkha, Alireza Keykha, Maysam Gholampoor, Maysam Lakzaee, Fereshteh Javadian
Zahedan Journal of Research in Medical Sciences.2026;[Epub] CrossRef

What impacts students’ satisfaction the most from Medicine Student Experience Questionnaire in Australia: a validity study: Pin-Hsiang Huang, Gary Velan, Greg Smith, Melanie Fentoullis, Sean Edward Kennedy, Karen Jane Gibson, Kerry Uebel, Boaz Shulruf; J Educ Eval Health Prof. 2023;20:2. Published online January 18, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.2

5,720 View
211 Download
6 Web of Science
5 Crossref

Abstract PDF Supplementary Material

Purpose
This study evaluated the validity of student feedback derived from Medicine Student Experience Questionnaire (MedSEQ), as well as the predictors of students’ satisfaction in the Medicine program.
Methods
Data from MedSEQ applying to the University of New South Wales Medicine program in 2017, 2019, and 2021 were analyzed. Confirmatory factor analysis (CFA) and Cronbach’s α were used to assess the construct validity and reliability of MedSEQ respectively. Hierarchical multiple linear regressions were used to identify the factors that most impact students’ overall satisfaction with the program.
Results
A total of 1,719 students (34.50%) responded to MedSEQ. CFA showed good fit indices (root mean square error of approximation=0.051; comparative fit index=0.939; chi-square/degrees of freedom=6.429). All factors yielded good (α>0.7) or very good (α>0.8) levels of reliability, except the “online resources” factor, which had acceptable reliability (α=0.687). A multiple linear regression model with only demographic characteristics explained 3.8% of the variance in students’ overall satisfaction, whereas the model adding 8 domains from MedSEQ explained 40%, indicating that 36.2% of the variance was attributable to students’ experience across the 8 domains. Three domains had the strongest impact on overall satisfaction: “being cared for,” “satisfaction with teaching,” and “satisfaction with assessment” (β=0.327, 0.148, 0.148, respectively; all with P<0.001).
Conclusion
MedSEQ has good construct validity and high reliability, reflecting students’ satisfaction with the Medicine program. Key factors impacting students’ satisfaction are the perception of being cared for, quality teaching irrespective of the mode of delivery and fair assessment tasks which enhance learning.

Citations

Citations to this article as recorded by

Mentor‐Student Relationship and Graduate Students' Satisfaction With Mentors: A Moderated Mediation Model
Xingzi Chen, Jiaqian Song, Hanjing Wen, Liuyi Zhang
Journal of Advanced Nursing.2026; 82(5): 4933. CrossRef
Pre-qualifications, learning strategies, and study satisfaction among medical students: insights from a multicenter German study
Carla Schröpel, Anne Herrmann-Werner, Teresa Festl-Wietek, Tim Wittenberg, Sabine C. Herpertz, Katrin Schüttpelz-Brauns, Andrea Heinzmann, Tobias Böckers, Stephan Zipfel, Rebecca Erschens
Medical Education Online.2026;[Epub] CrossRef
Implementing entrustable professional activities in a pre-certification medicine program: the Macquarie University approach
Catherine Mary Dean, Hayley Harris, Hugh Patrick McNeil, Chris Hughes
Higher Education, Skills and Work-Based Learning.2026; : 1. CrossRef
Linking quality perception to satisfaction in private universities: a mediated marketing model
Sk. Shahabuddin Ahmmed, Md. Sharif Hassan, Mohammad Bin Amin, Julinawati Binti Suanda, Shamsad Ahmed, Veronika Fenyves
Cogent Business & Management.2025;[Epub] CrossRef
Mental health and quality of life across 6 years of medical training: A year-by-year analysis
Natalia de Castro Pecci Maddalena, Alessandra Lamas Granero Lucchetti, Ivana Lucia Damasio Moutinho, Oscarina da Silva Ezequiel, Giancarlo Lucchetti
International Journal of Social Psychiatry.2024; 70(2): 298. CrossRef

Brief report

Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study: Sun Huh; J Educ Eval Health Prof. 2023;20:1. Published online January 11, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.1

24,594 View
1,259 Download
267 Web of Science
129 Crossref

Abstract PDF Supplementary Material

This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.

Citations

Citations to this article as recorded by

Is ChatGPT Enhancing Youth’s Learning, Engagement and Satisfaction?
Christina Sanchita Shah, Smriti Mathur, Sushant Kr. Vishnoi
Journal of Computer Information Systems.2026; 66(3): 353. CrossRef
Unveiling the impact of ChatGPT: investigating self-efficacy, anxiety and motivation on student performance in blended learning environments
Ridwan Daud Mahande, M. Miftach Fakhri, Irwansyah Suwahyu, Dwi Rezky Anandari Sulaiman
Journal of Applied Research in Higher Education.2026; 18(1): 282. CrossRef
Evaluation of the performance of ChatGPT‐4 and ChatGPT‐4o as a learning tool in endodontics
Esra Arılı Öztürk, Ceren Turan Gökduman, Burhan Can Çanakçi
International Endodontic Journal.2026; 59(6): 1057. CrossRef
Empirical investigation of critical factors influencing ChatGPT integration in contemporary technology education
Ming Yuan Hsieh
Research in Science & Technological Education.2026; 44(2): 641. CrossRef
Comparison of Human and Artificial Intelligence (AI) in Writing and Rating Restorative Dentistry Essays
Afnan O. Al‐Zain, Abdulrahman A. Alghamdi, Bashair Alansari, Alanoud Alamoudi, Heba El‐Deeb, Eman H. Ismail
European Journal of Dental Education.2026; 30(3): 1026. CrossRef
ChatGPT usage among nursing students in Taiwan: a qualitative study
Hui-Man Huang
BMC Nursing.2026;[Epub] CrossRef
Comparative performance of ChatGPT, Gemini, and Deepseek on endodontic exam questions in Turkish and English
Eda Gürsu Şahin
BMC Oral Health.2026;[Epub] CrossRef
Generative artificial intelligence in mental health: A preliminary study on automating materials development for cognitive bias modification
Che-Wei Hsu, Mia Cochrane, Sasini Bambarawana
International Journal of Mental Health.2026; : 1. CrossRef
Multi-metric comparative evaluation of DeepSeek and ChatGPT in USMLE versus CNMLE for medical education
Qing Wang, Junlian Li, Xiaoying Li, Panpan Deng
Scientific Reports.2026;[Epub] CrossRef
Assessing the accuracy and educational value of ChatGPT-generated content for core topics in cardiology: a descriptive analysis at Selçuk University Cardiology Clinic
Hüseyin Tezcan, Abdullah Tunçez, Kadri Murat Gürses, Yasin Özen, Muhammed Ulvi Yalçın
BMC Medical Education.2026;[Epub] CrossRef
Evaluation of the approaches of different specialties and artificial intelligence systems on traumatic dental injuries
Gonca Zelal Şahin, Ayhan Eymirli
BMC Oral Health.2026;[Epub] CrossRef
Artificial Intelligence for Assessment and Feedback in Medical Education: Bibliometric Mapping Study and Thematic Evidence Map
Zihang Zhao, Zihan Liu, Liang Guo, Teng Pan, Yousheng Zhang, Chenxiang Miao, Yiting Ge, Yipeng Wang, Xin Hu, Xin Wang, Ruipeng Zhang, Zhiyong Hou
JMIR Medical Education.2026; 12: e98949. CrossRef
ChatGPT and the AI revolution: a comprehensive investigation of its multidimensional impact and potential
Mohd Afjal
Library Hi Tech.2025; 43(1): 353. CrossRef
A comparative vignette study: Evaluating the potential role of a generative AI model in enhancing clinical decision‐making in nursing
Mor Saban, Ilana Dubovi
Journal of Advanced Nursing.2025; 81(11): 7489. CrossRef
Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation‐based clinical cases
Chedva Levin, Moriya Suliman, Etti Naimi, Mor Saban
Journal of Clinical Nursing.2025; 34(7): 2898. CrossRef
Exploring the Current Applications and Effectiveness of ChatGPT in Nursing: An Integrative Review
Yuan Luo, Yiqun Miao, Yuhan Zhao, Jiawei Li, Ying Wu
Journal of Advanced Nursing.2025; 81(7): 3473. CrossRef
ChatGPT-Produced Content as a Resource in the Language Education Classroom: A Guiding Hand
Rod E. Case, Leping Liu
Computers in the Schools.2025; 42(2): 187. CrossRef
Utility of ChatGPT as a preparation tool for the Orthopaedic In‐Training Examination
Dhruv Mendiratta, Isabel Herzog, Rohan Singh, Ashok Para, Tej Joshi, Michael Vosbikian, Neil Kaushal
Journal of Experimental Orthopaedics.2025;[Epub] CrossRef
Exploring knowledge, attitudes, and practices of academics in the field of educational sciences towards using ChatGPT
Burcu Karafil, Ahmet Uyar
Education and Information Technologies.2025; 30(9): 11649. CrossRef
Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM
Linlin Hu, Hao Wang, Yunfei Xin
Education and Information Technologies.2025; 30(9): 12609. CrossRef
Integrating AI Technology Into Language Teacher Education: Challenges, Potentials, and Assumptions
Rod Case, Leping Liu, Joseph Mintz
Computers in the Schools.2025; 42(2): 93. CrossRef
Performance of ChatGPT-3.5 and ChatGPT-4 in the Taiwan National Pharmacist Licensing Examination: Comparative Evaluation Study
Ying-Mei Wang, Hung-Wei Shen, Tzeng-Ji Chen, Shu-Chiung Chiang, Ting-Guan Lin
JMIR Medical Education.2025; 11: e56850. CrossRef
Eight Months into Reality: A Scoping Review of the Application of ChatGPT in Higher Education Teaching and Learning
Qian Liu, Anjin Hu, Tehmina Gladman, Steve Gallagher
Innovative Higher Education.2025; 50(5): 1677. CrossRef
Performance of artificial intelligence on Turkish dental specialization exam: can ChatGPT-4.0 and gemini advanced achieve comparable results to humans?
Soner Sismanoglu, Belen Sirinoglu Capan
BMC Medical Education.2025;[Epub] CrossRef
Applications of Artificial Intelligence in Medical Education: A Systematic Review
Eric Hallquist, Ishank Gupta, Michael Montalbano, Marios Loukas
Cureus.2025;[Epub] CrossRef
Performance of ChatGPT-4 on Taiwanese Traditional Chinese Medicine Licensing Examinations: Cross-Sectional Study
Liang-Wei Tseng, Yi-Chin Lu, Liang-Chi Tseng, Yu-Chun Chen, Hsing-Yu Chen
JMIR Medical Education.2025; 11: e58897. CrossRef
Comparing diagnostic skills in endodontic cases: dental students versus ChatGPT-4o
Parla Meva Durmazpinar, Ece Ekmekci
BMC Oral Health.2025;[Epub] CrossRef
Evaluating the agreement between ChatGPT-4 and validated questionnaires in screening for anxiety and depression in college students: a cross-sectional study
Jiali Liu, Juan Gu, Mengjie Tong, Yake Yue, Yufei Qiu, Lijuan Zeng, Yiqing Yu, Fen Yang, Shuyan Zhao
BMC Psychiatry.2025;[Epub] CrossRef
Factors Associated with Lower Performance of Artificial Intelligence on Answering Undergraduate Medical Education Multiple-Choice Questions
Renato Ferretti, Joyce Santana Rizzi, Lorraine Silva Requena, Angelica Maria Bicudo, Pedro Tadao Hamamoto Filho
Medical Science Educator.2025; 35(4): 2145. CrossRef
ChatGPT는 건강운동관리사 이론시험에 합격할 수 있을까?
상훈 김
The Korean Journal of Physical Education.2025; 64(2): 15. CrossRef
Assessing Information Provided by ChatGPT: Heart Failure Versus Patent Ductus Arteriosus
Meghana Bhupathi, Jaza Mehweish Kareem, Anjali Mediboina, Keerthana Janapareddy
Cureus.2025;[Epub] CrossRef
How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?
Sun Huh
Annals of Coloproctology.2025; 41(3): 190. CrossRef
Identification and Categorization of the Top 100 Articles and the Future of Large Language Models: Thematic Analysis Using Bibliometric Analysis
Ethan Bernstein, Anya Ramsamooj, Kelsey L Millar, Zachary C Lum
JMIR AI.2025; 4: e68603. CrossRef
Determinants of researchers’ intentions to utilize ChatGPT: insights from Türkiye and the United Kingdom
Nazli Yuceol, Ayse Merve Urfa Yilmaz, Meryem Akin
Journal of Cultural Cognitive Science.2025; 9(2): 331. CrossRef
The application of artificial intelligence-generated content in ophthalmology education
Yinzongxiao Wang, Yun Zhao, Jia Li
Frontiers in Medicine.2025;[Epub] CrossRef
Evaluating ChatGPT's Diagnostic Accuracy in Oral Mucosal Lesions: A Comparative Study with a Maxillofacial Surgeon
Hacer Eberliköse, Arif Yiğit Güler, Raha Akbarihamed, Caner Öztürk, Hakan Alpay Karasu
European Annals of Dental Sciences.2025; 52(2): 92. CrossRef
ChatGPT in Medical Education: Bibliometric and Visual Analysis
Yuning Zhang, Xiaolu Xie, Qi Xu
JMIR Medical Education.2025; 11: e72356. CrossRef
Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review
Yuhang Lin, Zhiheng Luo, Zicheng Ye, Nuoxi Zhong, Lijian Zhao, Long Zhang, Xiaolan Li, Zetao Chen, Yijia Chen
JMIR Medical Education.2025; 11: e71125. CrossRef
How AI Is Transforming Medical Education: Bibliometric Analysis
Youyang Wang, Chuheng Chang, Wen Shi, Huiting Liu, Xiaoming Huang, Yang Jiao
JMIR Medical Education.2025; 11: e75911. CrossRef
Chatbot Underperformance in Biology and Image-Based Questions in Medical Education
Joyce Santana Rizzi, Lorraine Silva Requena, Angelica Maria Bicudo, Pedro Tadao Hamamoto Filho, Renato Ferretti
Journal of CME.2025;[Epub] CrossRef
Evaluation of the accuracy of large language models in answering bone cancer-related questions
Qilin Pan, Leiwen Huang, Ning Liu, Feixiang Lin, Shuxi Ye
Frontiers in Public Health.2025;[Epub] CrossRef
Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education
Zhaoxiang Wen, Jiaxin Huang, Keer Yu, Yaqi Li, Zhenhui Wang, Xiaozhu Liao, Biao Li, Zhendong Tao, Hong He
Scientific Reports.2025;[Epub] CrossRef
Comparison of chatbots’ accuracy in endodontics questions in dentistry specialization exam in Türkiye: ChatGPT-4o, Gemini Advanced, Copilot, and Claude
Ceren Turan Gökduman, Esra Arılı Öztürk, Şule Aktaş, Burhan Can Çanakçi̇
BMC Oral Health.2025;[Epub] CrossRef
Multiple Large Language Models’ Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study
Yanyu Diao, Mengyuan Wu, Jingwen Xu, Yifeng Pan
JMIR Human Factors.2025; 12: e77978. CrossRef
Performance of ChatGPT on the India Undergraduate Community Medicine Examination: Cross-Sectional Study
Aravind P Gandhi, Felista Karen Joesph, Vineeth Rajagopal, P Aparnavi, Sushma Katkuri, Sonal Dayama, Prakasini Satapathy, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Ashish Behera
JMIR Formative Research.2024; 8: e49964. CrossRef
Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT
Jad Abi-Rafeh, Hong Hao Xu, Roy Kazan, Ruth Tevlin, Heather Furnas
Aesthetic Surgery Journal.2024; 44(3): 329. CrossRef
Redesigning Tertiary Educational Evaluation with AI: A Task-Based Analysis of LIS Students’ Assessment on Written Tests and Utilizing ChatGPT at NSTU
Shamima Yesmin
Science & Technology Libraries.2024; 43(4): 355. CrossRef
Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers
Ana Suárez, Víctor Díaz‐Flores García, Juan Algar, Margarita Gómez Sánchez, María Llorente de Pedro, Yolanda Freire
International Endodontic Journal.2024; 57(1): 108. CrossRef
Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment
Mike Richards, Kevin Waugh, Mark Slaymaker, Marian Petre, John Woodthorpe, Daniel Gooch
ACM Transactions on Computing Education.2024; 24(1): 1. CrossRef
A systematic review of ChatGPT use in K‐12 education
Peng Zhang, Gemma Tur
European Journal of Education.2024;[Epub] CrossRef
Evaluating ChatGPT as a self‐learning tool in medical biochemistry: A performance assessment in undergraduate medical university examination
Krishna Mohan Surapaneni, Anusha Rajajagadeesan, Lakshmi Goudhaman, Shalini Lakshmanan, Saranya Sundaramoorthi, Dineshkumar Ravi, Kalaiselvi Rajendiran, Porchelvan Swaminathan
Biochemistry and Molecular Biology Education.2024; 52(2): 237. CrossRef
Examining the use of ChatGPT in public universities in Hong Kong: a case study of restricted access areas
Michelle W. T. Cheng, Iris H. Y. YIM
Discover Education.2024;[Epub] CrossRef
Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study
Firas Haddad, Joanna S Saade
JMIR Medical Education.2024; 10: e50842. CrossRef
Assessment of Artificial Intelligence Platforms With Regard to Medical Microbiology Knowledge: An Analysis of ChatGPT and Gemini
Jai Ranjan, Absar Ahmad, Monalisa Subudhi, Ajay Kumar
Cureus.2024;[Epub] CrossRef
Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study
Annika Meyer, Janik Riese, Thomas Streichert
JMIR Medical Education.2024; 10: e50965. CrossRef
From hype to insight: Exploring ChatGPT's early footprint in education via altmetrics and bibliometrics
Lung‐Hsiang Wong, Hyejin Park, Chee‐Kit Looi
Journal of Computer Assisted Learning.2024; 40(4): 1428. CrossRef
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Morris Gordon, Michelle Daniel, Aderonke Ajiboye, Hussein Uraiby, Nicole Y. Xu, Rangana Bartlett, Janice Hanson, Mary Haas, Maxwell Spadafore, Ciaran Grafton-Clarke, Rayhan Yousef Gasiea, Colin Michie, Janet Corral, Brian Kwan, Diana Dolmans, Satid Thamma
Medical Teacher.2024; 46(4): 446. CrossRef
Üniversite Öğrencilerinin ChatGPT 3,5 Deneyimleri: Yapay Zekâyla Yazılmış Masal Varyantları
Bilge GÖK, Fahri TEMİZYÜREK, Özlem BAŞ
Korkut Ata Türkiyat Araştırmaları Dergisi.2024; (14): 1040. CrossRef
Tracking ChatGPT Research: Insights From the Literature and the Web
Omar Mubin, Fady Alnajjar, Zouheir Trabelsi, Luqman Ali, Medha Mohan Ambali Parambil, Zhao Zou
IEEE Access.2024; 12: 30518. CrossRef
Potential applications of ChatGPT in obstetrics and gynecology in Korea: a review article
YooKyung Lee, So Yun Kim
Obstetrics & Gynecology Science.2024; 67(2): 153. CrossRef
Application of generative language models to orthopaedic practice
Jessica Caterson, Olivia Ambler, Nicholas Cereceda-Monteoliva, Matthew Horner, Andrew Jones, Arwel Tomos Poacher
BMJ Open.2024; 14(3): e076484. CrossRef
Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef
The advent of ChatGPT: Job Made Easy or Job Loss to Data Analysts
Abiola Timothy Owolabi, Oluwaseyi Oluwadamilare Okunlola, Emmanuel Taiwo Adewuyi, Janet Iyabo Idowu, Olasunkanmi James Oladapo
WSEAS TRANSACTIONS ON COMPUTERS.2024; 23: 24. CrossRef
ChatGPT in dentomaxillofacial radiology education
Hilal Peker Öztürk, Hakan Avsever, Buğra Şenel, Şükran Ayran, Mustafa Çağrı Peker, Hatice Seda Özgedik, Nurten Baysal
Journal of Health Sciences and Medicine.2024; 7(2): 224. CrossRef
Performance of ChatGPT on the Korean National Examination for Dental Hygienists
Soo-Myoung Bae, Hye-Rim Jeon, Gyoung-Nam Kim, Seon-Hui Kwak, Hyo-Jin Lee
Journal of Dental Hygiene Science.2024; 24(1): 62. CrossRef
Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance
Sultan Ayoub Meo, Metib Alotaibi, Muhammad Zain Sultan Meo, Muhammad Omair Sultan Meo, Mashhood Hamid
Frontiers in Public Health.2024;[Epub] CrossRef
Unlock the potential for Saudi Arabian higher education: a systematic review of the benefits of ChatGPT
Eman Faisal
Frontiers in Education.2024;[Epub] CrossRef
Does the Information Quality of ChatGPT Meet the Requirements of Orthopedics and Trauma Surgery?
Adnan Kasapovic, Thaer Ali, Mari Babasiz, Jessica Bojko, Martin Gathen, Robert Kaczmarczyk, Jonas Roos
Cureus.2024;[Epub] CrossRef
Exploring the Profile of University Assessments Flagged as Containing AI-Generated Material
Daniel Gooch, Kevin Waugh, Mike Richards, Mark Slaymaker, John Woodthorpe
ACM Inroads.2024; 15(2): 39. CrossRef
Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom’s Taxonomy
Ambadasu Bharatha, Nkemcho Ojeh, Ahbab Mohammad Fazle Rabbi, Michael Campbell, Kandamaran Krishnamurthy, Rhaheem Layne-Yarde, Alok Kumar, Dale Springer, Kenneth Connell, Md Anwarul Majumder
Advances in Medical Education and Practice.2024; Volume 15: 393. CrossRef
The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary
Sun Huh
Journal of Educational Evaluation for Health Professions.2024; 21: 9. CrossRef
ChatGPT, a Friend or a Foe in Medical Education: A Review of Strengths, Challenges, and Opportunities
Mahdi Zarei, Maryam Zarei, Sina Hamzehzadeh, Sepehr Shakeri Bavil Oliyaei, Mohammad-Salar Hosseini
Shiraz E-Medical Journal.2024;[Epub] CrossRef
Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome
Farah Naja, Mandy Taktouk, Dana Matbouli, Sharfa Khaleel, Ayah Maher, Berna Uzun, Maryam Alameddine, Lara Nasreddine
European Journal of Clinical Nutrition.2024; 78(10): 887. CrossRef
Large language models in healthcare: from a systematic review on medical examinations to a comparative analysis on fundamentals of robotic surgery online test
Andrea Moglia, Konstantinos Georgiou, Pietro Cerveri, Luca Mainardi, Richard M. Satava, Alfred Cuschieri
Artificial Intelligence Review.2024;[Epub] CrossRef
Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum
Annika Meyer, Ari Soleman, Janik Riese, Thomas Streichert
Clinical Chemistry and Laboratory Medicine (CCLM).2024;[Epub] CrossRef
Performance of ChatGPT-3.5 and GPT-4 in national licensing examinations for medicine, pharmacy, dentistry, and nursing: a systematic review and meta-analysis
Hye Kyung Jin, Ha Eun Lee, EunYoung Kim
BMC Medical Education.2024;[Epub] CrossRef
Role of ChatGPT in Dentistry: A Review
Pratik Surana, Priyanka P. Ostwal, Shruti Vishal Dev, Jayesh Tiwari, Kadire Shiva Charan Yadav, Gajji Renuka
Research Journal of Pharmacy and Technology.2024; : 3489. CrossRef
A Scoping Review on the Educational Applications of Generative AI in Primary and Secondary Education
Solmoe Ahn, Jeongyoon Lee, Jungmin Park, Soyoung Jung, Jihoon Song
The Journal of Korean Association of Computer Education.2024; 27(6): 11. CrossRef
Performance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study
Hye Kyung Jin, EunYoung Kim
JMIR Medical Education.2024; 10: e57451. CrossRef
Evaluating the Feasibility of ChatGPT in Dental Morphology Education: A Pilot Study on AI-Assisted Learning in Dental Morphology
Eun-Young Jeon, Hyun-Na Ahn, Jeong-Hyun Lee
Journal of Dental Hygiene Science.2024; 24(4): 309. CrossRef
Detecting AI- generated versus human- written medical student essays: a semi-randomized controlled study (Preprint)
Berin Doru, Christoph Maier, Johanna Sophie Busse, Thomas Lücke, Judith Schönhoff, Elena Enax- Krumova, Steffen Hessler, Maria Berger, Marianne Tokic
JMIR Medical Education.2024;[Epub] CrossRef
Is ChatGPT reliable in education?
Amal Abdullah Alibrahim
South African Journal of Education.2024; 44(4): 1. CrossRef
Advancing Scholarly Publishing Through Artificial Intelligence: A Paradigm Shift
Muskan Dubey, Arun Kumar Dubey , Ravindra P Veeranna
Trends in Scholarly Publishing.2024; 3(1): 1. CrossRef
Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence
Abubaker Qutieshat, Alreem Al Rusheidi, Samiya Al Ghammari, Abdulghani Alarabi, Abdurahman Salem, Maja Zelihic
Diagnosis.2024; 11(3): 259. CrossRef
Applicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology
Ranwir K Sinha, Asitava Deb Roy, Nikhil Kumar, Himel Mondal
Cureus.2023;[Epub] CrossRef
Issues in the 3rd year of the COVID-19 pandemic, including computer-based testing, study design, ChatGPT, journal metrics, and appreciation to reviewers
Sun Huh
Journal of Educational Evaluation for Health Professions.2023; 20: 5. CrossRef
Emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic
Sun Huh
Science Editing.2023; 10(1): 1. CrossRef
Assessing the Capability of ChatGPT in Answering First- and Second-Order Knowledge Questions on Microbiology as per Competency-Based Medical Education Curriculum
Dipmala Das, Nikhil Kumar, Langamba Angom Longjam, Ranwir Sinha, Asitava Deb Roy, Himel Mondal, Pratima Gupta
Cureus.2023;[Epub] CrossRef
Evaluating ChatGPT's Ability to Solve Higher-Order Questions on the Competency-Based Medical Education Curriculum in Medical Biochemistry
Arindam Ghosh, Aritri Bir
Cureus.2023;[Epub] CrossRef
Overview of Early ChatGPT’s Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts
Omar Temsah, Samina A Khan, Yazan Chaiah, Abdulrahman Senjab, Khalid Alhasan, Amr Jamal, Fadi Aljamaan, Khalid H Malki, Rabih Halwani, Jaffar A Al-Tawfiq, Mohamad-Hani Temsah, Ayman Al-Eyadhy
Cureus.2023;[Epub] CrossRef
ChatGPT for Future Medical and Dental Research
Bader Fatani
Cureus.2023;[Epub] CrossRef
ChatGPT in Dentistry: A Comprehensive Review
Hind M Alhaidry, Bader Fatani, Jenan O Alrayes, Aljowhara M Almana, Nawaf K Alfhaed
Cureus.2023;[Epub] CrossRef
Can we trust AI chatbots’ answers about disease diagnosis and patient care?
Sun Huh
Journal of the Korean Medical Association.2023; 66(4): 218. CrossRef
Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
Alaa Abd-alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, Padraig Mark Healy, Syed Latifi, Sarah Aziz, Rafat Damseh, Sadam Alabed Alrazak, Javaid Sheikh
JMIR Medical Education.2023; 9: e48291. CrossRef
Early applications of ChatGPT in medical practice, education and research
Sam Sedaghat
Clinical Medicine.2023; 23(3): 278. CrossRef
A Review of Research on Teaching and Learning Transformation under the Influence of ChatGPT Technology
璇师
Advances in Education.2023; 13(05): 2617. CrossRef
Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study
Soshi Takagi, Takashi Watari, Ayano Erabi, Kota Sakaguchi
JMIR Medical Education.2023; 9: e48002. CrossRef
ChatGPT’s quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions
Cosima C. Hoch, Barbara Wollenberg, Jan-Christoffer Lüers, Samuel Knoedler, Leonard Knoedler, Konstantin Frank, Sebastian Cotofana, Michael Alfertshofer
European Archives of Oto-Rhino-Laryngology.2023; 280(9): 4271. CrossRef
Analysing the Applicability of ChatGPT, Bard, and Bing to Generate Reasoning-Based Multiple-Choice Questions in Medical Physiology
Mayank Agarwal, Priyanka Sharma, Ayan Goswami
Cureus.2023;[Epub] CrossRef
The Intersection of ChatGPT, Clinical Medicine, and Medical Education
Rebecca Shin-Yee Wong, Long Chiau Ming, Raja Affendi Raja Ali
JMIR Medical Education.2023; 9: e47274. CrossRef
The Role of Artificial Intelligence in Higher Education: ChatGPT Assessment for Anatomy Course
Tarık TALAN, Yusuf KALINKARA
Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi.2023; 7(1): 33. CrossRef
Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency of stereotype attribution with those of medical students in New Zealand in developing a similarity rating test: a methodological study
Chao-Cheng Lin, Zaine Akuhata-Huntington, Che-Wei Hsu
Journal of Educational Evaluation for Health Professions.2023; 20: 17. CrossRef
Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation
Hsing-Yu Hsu, Kai-Cheng Hsu, Shih-Yen Hou, Ching-Lung Wu, Yow-Wen Hsieh, Yih-Dih Cheng
JMIR Medical Education.2023; 9: e48433. CrossRef
Assessing the Efficacy of ChatGPT in Solving Questions Based on the Core Concepts in Physiology
Arijita Banerjee, Aquil Ahmad, Payal Bhalla, Kavita Goyal
Cureus.2023;[Epub] CrossRef
ChatGPT Performs on the Chinese National Medical Licensing Examination
Xinyi Wang, Zhenye Gong, Guoxin Wang, Jingdan Jia, Ying Xu, Jialu Zhao, Qingye Fan, Shaun Wu, Weiguo Hu, Xiaoyang Li
Journal of Medical Systems.2023;[Epub] CrossRef
Artificial intelligence and its impact on job opportunities among university students in North Lima, 2023
Doris Ruiz-Talavera, Jaime Enrique De la Cruz-Aguero, Nereo García-Palomino, Renzo Calderón-Espinoza, William Joel Marín-Rodriguez
ICST Transactions on Scalable Information Systems.2023;[Epub] CrossRef
Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties
Najd Alzaid, Omar Ghulam, Modhi Albani, Rafa Alharbi, Mayan Othman, Hasan Taher, Saleem Albaradie, Suhael Ahmed
Cureus.2023;[Epub] CrossRef
Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review
Carl Preiksaitis, Christian Rose
JMIR Medical Education.2023; 9: e48785. CrossRef
Exploring the impact of language models, such as ChatGPT, on student learning and assessment
Araz Zirar
Review of Education.2023;[Epub] CrossRef
Evaluating the reliability of ChatGPT as a tool for imaging test referral: a comparative study with a clinical decision support system
Shani Rosen, Mor Saban
European Radiology.2023; 34(5): 2826. CrossRef
The Significance of Artificial Intelligence Platforms in Anatomy Education: An Experience With ChatGPT and Google Bard
Hasan B Ilgaz, Zehra Çelik
Cureus.2023;[Epub] CrossRef
Is ChatGPT’s Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination?
Abhra Ghosh, Nandita Maini Jindal, Vikram K Gupta, Ekta Bansal, Navjot Kaur Bajwa, Abhishek Sett
Cureus.2023;[Epub] CrossRef
Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article
Sun Huh
Child Health Nursing Research.2023; 29(4): 249. CrossRef
Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive Pilot Study
Osman Babayiğit, Zeynep Tastan Eroglu, Dilek Ozkan Sen, Fatma Ucan Yarkac
Cureus.2023;[Epub] CrossRef
Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
Aleksandra Ignjatović, Lazar Stevanović
Journal of Educational Evaluation for Health Professions.2023; 20: 28. CrossRef
Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case Vignettes: Observational Study
Krishna Mohan Surapaneni
JMIR Medical Education.2023; 9: e47191. CrossRef
Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study
Betzy Clariza Torres-Zegarra, Wagner Rios-Garcia, Alvaro Micael Ñaña-Cordova, Karen Fatima Arteaga-Cisneros, Xiomara Cristina Benavente Chalco, Marina Atena Bustamante Ordoñez, Carlos Jesus Gutierrez Rios, Carlos Alberto Ramos Godoy, Kristell Luisa Teresa
Journal of Educational Evaluation for Health Professions.2023; 20: 30. CrossRef
ChatGPT’s performance in German OB/GYN exams – paving the way for AI-enhanced medical education and clinical practice
Maximilian Riedel, Katharina Kaefinger, Antonia Stuehrenberg, Viktoria Ritter, Niklas Amann, Anna Graf, Florian Recker, Evelyn Klein, Marion Kiechle, Fabian Riedel, Bastian Meyer
Frontiers in Medicine.2023;[Epub] CrossRef
Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study
Janghee Park
Journal of Educational Evaluation for Health Professions.2023; 20: 29. CrossRef
FROM TEXT TO DIAGNOSE: CHATGPT’S EFFICACY IN MEDICAL DECISION-MAKING
Yaroslav Mykhalko, Pavlo Kish, Yelyzaveta Rubtsova, Oleksandr Kutsyn, Valentyna Koval
Wiadomości Lekarskie.2023; 76(11): 2345. CrossRef
Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions
Pasin Tangadulrat, Supinya Sono, Boonsin Tangtrakulwanich
JMIR Medical Education.2023; 9: e50658. CrossRef
Below average ChatGPT performance in medical microbiology exam compared to university students
Malik Sallam, Khaled Al-Salahat
Frontiers in Education.2023;[Epub] CrossRef
ChatGPT: "To be or not to be" ... in academic research. The human mind's analytical rigor and capacity to discriminate between AI bots' truths and hallucinations
Aurelian Anghelescu, Ilinca Ciobanu, Constantin Munteanu, Lucia Ana Maria Anghelescu, Gelu Onose
Balneo and PRM Research Journal.2023; 14(Vol.14, no): 614. CrossRef
ChatGPT Review: A Sophisticated Chatbot Models in Medical & Health-related Teaching and Learning
Nur Izah Ab Razak, Muhammad Fawwaz Muhammad Yusoff, Rahmita Wirza O.K. Rahmat
Malaysian Journal of Medicine and Health Sciences.2023; 19(s12): 98. CrossRef
Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
Tae Won Kim
Journal of Educational Evaluation for Health Professions.2023; 20: 38. CrossRef
Trends in research on ChatGPT and adoption-related issues discussed in articles: a narrative review
Sang-Jun Kim
Science Editing.2023; 11(1): 3. CrossRef
Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soobin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef
What will ChatGPT revolutionize in the financial industry?
Hassnian Ali, Ahmet Faruk Aysan
Modern Finance.2023; 1(1): 116. CrossRef
Performance of a Large Language Model in Medical Pharmacology Education: An Assessment Using Multiple-Choice Questions
Benjamin S. Wright, Laura J. Kim, Nathan R. Coleman
Annals of Pharmacy Education, Safety, and Public Health Advocacy.2023; 3(1): 232. CrossRef

Reviews

Factors associated with medical students’ scores on the National Licensing Exam in Peru: a systematic review: Javier Alejandro Flores-Cohaila; J Educ Eval Health Prof. 2022;19:38. Published online December 29, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.38

9,537 View
407 Download
4 Web of Science
7 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to identify factors that have been studied for their associations with National Licensing Examination (ENAM) scores in Peru.
Methods
A search was conducted of literature databases and registers, including EMBASE, SciELO, Web of Science, MEDLINE, Peru’s National Register of Research Work, and Google Scholar. The following key terms were used: “ENAM” and “associated factors.” Studies in English and Spanish were included. The quality of the included studies was evaluated using the Medical Education Research Study Quality Instrument (MERSQI).
Results
In total, 38,500 participants were enrolled in 12 studies. Most (11/12) studies were cross-sectional, except for one case-control study. Three studies were published in peer-reviewed journals. The mean MERSQI was 10.33. A better performance on the ENAM was associated with a higher-grade point average (GPA) (n=8), internship setting in EsSalud (n=4), and regular academic status (n=3). Other factors showed associations in various studies, such as medical school, internship setting, age, gender, socioeconomic status, simulations test, study resources, preparation time, learning styles, study techniques, test-anxiety, and self-regulated learning strategies.
Conclusion
The ENAM is a multifactorial phenomenon; our model gives students a locus of control on what they can do to improve their score (i.e., implement self-regulated learning strategies) and faculty, health policymakers, and managers a framework to improve the ENAM score (i.e., design remediation programs to improve GPA and integrate anxiety-management courses into the curriculum).

Citations

Citations to this article as recorded by

Anxiety and Depression Associated With the Dependent Use of Generative AI in Medical Students: Cross-Sectional Study
Janett V Chavez Sosa, Salomon Huancahuire-Vega
JMIR Formative Research.2026; 10: e82667. CrossRef
Abuse and empowerment in the academic field on the academic performance of medical students: a cross-sectional study
Esteban G. Reyes, Matheus Pierini Santos, Edda E. Newball-Noriega, Salomón Huancahuire-Vega
BMC Medical Education.2026;[Epub] CrossRef
Peruvian medical residency selection: a portrayal of scores, distribution, and predictors of 28,872 applicants between 2019 and 2023
Javier A. Flores-Cohaila, Brayan Miranda-Chavez, Cesar Copaja-Corzo, Xiomara C. Benavente-Chalco, Wagner Rios-García, Vanessa P. Moreno-Ccama, Angel Samanez-Obeso, Marco Rivarola-Hidalgo
BMC Medical Education.2025;[Epub] CrossRef
Puntajes en pruebas de progreso como predictores del desempeño en el Examen Nacional de Medicina del Perú
Franco Romaní, César Gutiérrez
Educación Médica.2025; 26(6): 101092. CrossRef
Predicción del éxito en el examen de habilitación profesional: un modelo de regresión logística basado en variables multifactoriales
Saul Yasaca Pucuna, Juan Diego Erazo Rodríguez
Revista de Investigación Educativa Niveles.2025; 2(2): 36. CrossRef
Medical Student’s Attitudes towards Implementation of National Licensing Exam (NLE) – A Qualitative Exploratory Study
Saima Bashir, Rehan Ahmed Khan
Pakistan Journal of Health Sciences.2024; : 153. CrossRef
Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study
Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga Torres, Alvaro Taype-Rondan
JMIR Medical Education.2023; 9: e48039. CrossRef

Medical students’ satisfaction level with e-learning during the COVID-19 pandemic and its related factors: a systematic review: Mahbubeh Tabatabaeichehr, Samane Babaei, Mahdieh Dartomi, Peiman Alesheikh, Amir Tabatabaee, Hamed Mortazavi, Zohreh Khoshgoftar; J Educ Eval Health Prof. 2022;19:37. Published online December 20, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.37

7,275 View
300 Download
24 Web of Science
26 Crossref

Abstract PDF Supplementary Material

Purpose
This review investigated medical students’ satisfaction level with e-learning during the coronavirus disease 2019 (COVID-19) pandemic and its related factors.
Methods
A comprehensive systematic search was performed of international literature databases, including Scopus, PubMed, Web of Science, and Persian databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Distance learning,” “Distance education,” “Online learning,” “Online education,” and “COVID-19” from the earliest date to July 10, 2022. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
Results
A total of 15,473 medical science students were enrolled in 24 studies. The level of satisfaction with e-learning during the COVID-19 pandemic among medical science students was 51.8%. Factors such as age, gender, clinical year, experience with e-learning before COVID-19, level of study, adaptation content of course materials, interactivity, understanding of the content, active participation of the instructor in the discussion, multimedia use in teaching sessions, adequate time dedicated to the e-learning, stress perception, and convenience had significant relationships with the satisfaction of medical students with e-learning during the COVID-19 pandemic.
Conclusion
Therefore, due to the inevitability of online education and e-learning, it is suggested that educational managers and policymakers choose the best online education method for medical students by examining various studies in this field to increase their satisfaction with e-learning.

Citations

Citations to this article as recorded by

Application of Analytic Hierarchy Process and fuzzy evaluation method in gamification satisfaction assessment of online education platforms: a case study of NetEase Cloud Classroom
Wenjing Wei, Xiaodong Gong
Technology, Pedagogy and Education.2026; 35(3): 445. CrossRef
Medical students’ mental health, quality of life, motivation, and learning approaches before, during and after COVID-19: findings from a 4-wave repeated cross-sectional survey
Maria Do Carmo Mattos Martins, Giancarlo Lucchetti, João Pedro Torres Neiva Rodrigues, Raquel Vieira Torres, Mariana Brandão Sampaio, Isadora de Cássia Oliveira Leal, Vitória Mesquita Campos Mendes, Jéssica Aparecida Gomes Cotta, Natalia Castro Pecci Madd
Psychology, Health & Medicine.2026; : 1. CrossRef
Stress, mental health, and adaptation among medical students during educational disruption due to pandemic: a mixed-methods study
Tarron Kayalackakom, Dheeraj Baji, Sheetal Naik, Nkechi Ikediobi, Natalie Butler, Edmond Mansoor, Neville Fernandez, Prasanna Honnavar
Frontiers in Education.2026;[Epub] CrossRef
Virtual global health education partnerships for health professional students: a scoping review
Nora K. Lenhard, Crystal An, Divya Jasthi, Veronica Laurel-Vargas, Ilon Weinstein, Suet K. Lam
Global Health Promotion.2025; 32(2): 14. CrossRef
Applying the Panarchy Framework to Examining Post-Pandemic Adaptation in the Undergraduate Medical Education Environment: A Qualitative Study
Gowda Parameshwara Prashanth, Ciraj Ali Mohammed
Teaching and Learning in Medicine.2025; 37(5): 654. CrossRef
Medical students’ perceptions of the post-COVID-19 educational environment in Oman
Gowda Parameshwara Prashanth, Ciraj Ali Mohammed
Learning Environments Research.2025; 28(1): 1. CrossRef
Student perceptions and outcomes from asynchronous versus synchronous remote learning in a pharmacy skills course
Stephanie L. Hsia, Gabriela Mackey, Raka Mondal, Crystal Zhou
Currents in Pharmacy Teaching and Learning.2025; 17(5): 102299. CrossRef
Identifying Research Priorities in Digital Education for Health Care: Umbrella Review and Modified Delphi Method Study
Alison Potter, Chris Munsch, Elaine Watson, Emily Hopkins, Sofia Kitromili, Iain Cameron O'Neill, Judy Larbie, Essi Niittymaki, Catriona Ramsay, Joshua Burke, Neil Ralph
Journal of Medical Internet Research.2025; 27: e66157. CrossRef
Assessing student satisfaction with university policies related to in-person classes in the era of COVID-19: a case study from Korea
Sungyo Jung, Yoojin Cho, Jinhyun Kwon, Yeram Yang, Jaewon Lee, Sungkyoon Kim
Scientific Reports.2025;[Epub] CrossRef
Psychological empowerment and proactive work behavior: a meta-analysis study
Pratista Arya Satwika, Fendy Suhariadi, Samian Samian
Management Review Quarterly.2025;[Epub] CrossRef
Digital health economics education: perspectives, potential and barriers at German medical universities
Stefan Hertling, Oliver Schöffski, Isabel Graul, Ekkehard Schleußner
Frontiers in Medicine.2025;[Epub] CrossRef
Higher Education Management of the COVID-19 Pandemic: Student Perceptions and Satisfaction
Maria Eliophotou Menon
European Journal of Educational Management.2025; 8(3): 173. CrossRef
A NEW CHALLENGE IN DISTANCE EDUCATION: COGNITIVE TEST ANXIETY AND RELATED STUDENT EXPERIENCES
Gizem Beycan Ekitli, Gamze Gale
EKEV Akademi Dergisi.2025; (104): 19. CrossRef
Factors Influencing EFL College Students’ Continuance Intention in Online Learning: The Mediating Roles of Trust and Flow Experience
Hanhui Li, Fan Yang, Jinpeng Liu
Sage Open.2025;[Epub] CrossRef
Who Thrives in Medical School? Intrinsic Motivation, Resilience, and Satisfaction Among Medical Students
Julia Terech, Pola Sarnowska, Klaudia Bikowska, Mateusz Guziak, Maciej Walkiewicz
Healthcare.2025; 13(23): 3049. CrossRef
Factors affecting medical students’ satisfaction with online learning: a regression analysis of a survey
Özlem Serpil Çakmakkaya, Elif Güzel Meydanlı, Ali Metin Kafadar, Mehmet Selman Demirci, Öner Süzer, Muhlis Cem Ar, Muhittin Onur Yaman, Kaan Can Demirbaş, Mustafa Sait Gönen
BMC Medical Education.2024;[Epub] CrossRef
A comparative study on the effectiveness of online and in-class team-based learning on student performance and perceptions in virtual simulation experiments
Jing Shen, Hongyan Qi, Ruhuan Mei, Cencen Sun
BMC Medical Education.2024;[Epub] CrossRef
Pharmacy Students’ Attitudes Toward Distance Learning After the COVID-19 Pandemic: Cross-Sectional Study From Saudi Arabia
Saud Alsahali, Salman Almutairi, Salem Almutairi, Saleh Almofadhi, Mohammed Anaam, Mohammed Alshammari, Suhaj Abdulsalim, Yasser Almogbel
JMIR Formative Research.2024; 8: e54500. CrossRef
Effects of the First Wave of the COVID-19 Pandemic on the Work Readiness of Undergraduate Nursing Students in China: A Mixed-Methods Study
Lifang He, Jean Rizza Dela Cruz
Risk Management and Healthcare Policy.2024; Volume 17: 559. CrossRef
Online learning satisfaction and participation in flipped classroom and case-based learning for medical students
Irma Uliano Effting Zoch de Moura, Valentina Coutinho Baldoto Gava Chakr
Revista Brasileira de Educação Médica.2024;[Epub] CrossRef
Medical education during the coronavirus disease 2019 pandemic: an umbrella review
Seyed Aria Nejadghaderi, Zohreh Khoshgoftar, Asra Fazlollahi, Mohammad Javad Nasiri
Frontiers in Medicine.2024;[Epub] CrossRef
Exploration of the Education and Teaching Management Model for Medical International Students in China
兴亮代
Advances in Education.2024; 14(08): 390. CrossRef
Identifying group metacognition associated with medical students’ teamwork satisfaction in an online small group tutorial context
Chia-Ter Chao, Yen-Lin Chiu, Chiao-Ling Tsai, Mong-Wei Lin, Chih-Wei Yang, Chiao-Chi Ho, Chiun Hsu, Huey-Ling Chen
BMC Medical Education.2024;[Epub] CrossRef
Challenges and factors affecting online teaching during covid-19 in nursing and midwifery colleges in northern Ghana: a mixed methods study
Abdul-Malik Abdulai, Ibrahim Yakubu, Wuni Abubakari, Abdul-Aziz Baba, Beatrice Abanga, Aabier Lucy
The Evidence.2024;[Epub] CrossRef
Physician Assistant Students’ Perception of Online Didactic Education: A Cross-Sectional Study
Daniel L Anderson, Jeffrey L Alexander
Cureus.2023;[Epub] CrossRef
Mediating Role of PERMA Wellbeing in the Relationship between Insomnia and Psychological Distress among Nursing College Students
Qian Sun, Xiangyu Zhao, Yiming Gao, Di Zhao, Meiling Qi
Behavioral Sciences.2023; 13(9): 764. CrossRef

Brief report

Self-directed learning quotient and common learning types of pre-medical students in Korea by the Multi-Dimensional Learning Strategy Test 2nd edition: a descriptive study: Sun Kim, A Ra Cho, Chul Woon Chung; J Educ Eval Health Prof. 2022;19:32. Published online November 28, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.32

4,380 View
184 Download

Abstract PDF Supplementary Material: This study aimed to find the self-directed learning quotient and common learning types of pre-medical students through the confirmation of 4 characteristics of learning strategies, including personality, motivation, emotion, and behavior. The response data were collected from 277 out of 294 target first-year pre-medical students from 2019 to 2021, using the Multi-Dimensional Learning Strategy Test 2nd edition. The most common learning type was a self-directed type (44.0%), stagnant type (33.9%), latent type (14.4%), and conscientiousness type (7.6%). The self-directed learning index was high (29.2%), moderate (24.6%), somewhat high (21.7%), somewhat low (14.4%), and low (10.1%). This study confirmed that many students lacked self-directed learning capabilities for learning strategies. In addition, it was found that the difficulties experienced by each student were different, and the variables resulting in difficulties were also diverse. It may provide insights into how to develop programs that can help students increase their self-directed learning capability.

Research articles

Is online objective structured clinical examination teaching an acceptable replacement in post-COVID-19 medical education in the United Kingdom?: a descriptive study: Vashist Motkur, Aniket Bharadwaj, Nimalesh Yogarajah; J Educ Eval Health Prof. 2022;19:30. Published online November 7, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.30

6,111 View
183 Download
6 Web of Science
6 Crossref

Abstract PDF Supplementary Material

Purpose
Coronavirus disease 2019 (COVID-19) restrictions resulted in an increased emphasis on virtual communication in medical education. This study assessed the acceptability of virtual teaching in an online objective structured clinical examination (OSCE) series and its role in future education.
Methods
Six surgical OSCE stations were designed, covering common surgical topics, with specific tasks testing data interpretation, clinical knowledge, and communication skills. These were delivered via Zoom to students who participated in student/patient/examiner role-play. Feedback was collected by asking students to compare online teaching with previous experiences of in-person teaching. Descriptive statistics were used for Likert response data, and thematic analysis for free-text items.
Results
Sixty-two students provided feedback, with 81% of respondents finding online instructions preferable to paper equivalents. Furthermore, 65% and 68% found online teaching more efficient and accessible, respectively, than in-person teaching. Only 34% found communication with each other easier online; Forty percent preferred online OSCE teaching to in-person teaching. Students also expressed feedback in positive and negative free-text comments.
Conclusion
The data suggested that generally students were unwilling for online teaching to completely replace in-person teaching. The success of online teaching was dependent on the clinical skill being addressed; some were less amenable to a virtual setting. However, online OSCE teaching could play a role alongside in-person teaching.

Citations

Citations to this article as recorded by

Perception of online learning, knowledge assessment, and clinical skills among third-year ophthalmology residents studying during the COVID-19 pandemic in Thailand
Wanicha Chuenkongkaew, Jimethat Chalermpong, Weerawat Kiddee, Prut Hanutsaha, Anita Manassakorn, Olan Suwan-apichon, Sakchai Vongkittirux, Raveewan Choontanom, Pittaya Phamonvaechavan, Tharnsook Kasemsup, Paradee Kunavisarut, Sudawadee Somboonthanakij, Su
Scientific Reports.2026;[Epub] CrossRef
The assessment of clinical competence in medical students during the COVID-19 pandemic: a scoping review
Harry McGrath, Elysha Brennan, Dominic Harmon
Irish Journal of Medical Science (1971 -).2026;[Epub] CrossRef
Evaluation of a virtual objective structured clinical examination in the metaverse (Second Life) to assess the clinical skills in emergency radiology of medical students in Spain: a cross-sectional study
Alba Virtudes Perez-Baena, Teodoro Rudolphi-Solero, Rocio Lorenzo-Alvarez, Dolores Dominguez-Pinos, Miguel Jose Ruiz-Gomez, Francisco Sendra-Portero
Journal of Educational Evaluation for Health Professions.2025; 22: 12. CrossRef
A Comparative Study of Student Perspectives on Online Versus In-Person Objective Structured Clinical Examination (OSCE) Teaching at a Medical School in London
Nimalesh Yogarajah, Aniket Bharadwaj, Amelia Snook, Vashist Motkur
Cureus.2025;[Epub] CrossRef
Feasibility and reliability of the pandemic-adapted online-onsite hybrid graduation OSCE in Japan
Satoshi Hara, Kunio Ohta, Daisuke Aono, Toshikatsu Tamai, Makoto Kurachi, Kimikazu Sugimori, Hiroshi Mihara, Hiroshi Ichimura, Yasuhiko Yamamoto, Hideki Nomura
Advances in Health Sciences Education.2024; 29(3): 949. CrossRef
Should Virtual Objective Structured Clinical Examination (OSCE) Teaching Replace or Complement Face-to-Face Teaching in the Post-COVID-19 Educational Environment: An Evaluation of an Innovative National COVID-19 Teaching Programme
Charles Gamble, Alice Oatham, Raj Parikh
Cureus.2023;[Epub] CrossRef

Enhanced numeracy skills following team-based learning in United States pharmacy students: a longitudinal cohort study: Rob Edwin Carpenter, Leanne Coyne, Dave Silberman, Jody Kyoto Takemoto; J Educ Eval Health Prof. 2022;19:29. Published online October 27, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.29

5,733 View
214 Download
2 Web of Science
4 Crossref

Abstract PDF Supplementary Material

Purpose
The literature suggests that the ability to numerate cannot be fully understood without accounting for the social context in which mathematical activity is represented. Team-based learning (TBL) is an andragogical approach with theoretical links to sociocultural and community-of-practice learning. This study aimed to quantitatively explore the impact of TBL instruction on numeracy development in 2 cohorts of pharmacy students and identify the impact of TBL instruction on numeracy development from a social perspective for healthcare education.
Methods
Two cohorts of students were administered the Health Science Reasoning Test-Numeracy (HSRT-N) before beginning pharmacy school. Two years after using TBL as the primary method of instruction, both comprehensive and domain data from the HSRT-N were analyzed.
Results
In total, 163 pharmacy student scores met the inclusion criteria. The students’ numeracy skills measured by HSRT-N improved after 2 years of TBL instruction.
Conclusion
Numeracy was the most significantly improved HSRT-N domain in pharmacy students following two years of TBL instruction. Although a closer examination of numeracy development in TBL is warranted, initial data suggest that TBL instruction may be an adequate proxy for advancing numeracy in a cohort of pharmacy students. TBL may encourage a social practice of mathematics to improve pharmacy students’ ability to numerate critically.

Citations

Citations to this article as recorded by

Critical Thinking Engagement and Assessment in Didactic Pharmacy Education: A Scoping Review
Anna Nogid, Jennifer Beall, Lisa Killam-Worrall, Erenie Guirguis
American Journal of Pharmaceutical Education.2026; 90(5): 101966. CrossRef
Team-Based Learning in Mathematics Education: A Literature Review
Nike Astiswijaya, Turmudi, Kusnandi, Dadang Juandi
Journal of Physics: Conference Series.2026; 3276(1): 012034. CrossRef
Hybrid Team-Based Learning-Personalized Education Teaching (HTBL-PE)—A New Teaching Strategy to Maximise Success
Tam Ha, Byron Kemp, Janine Delahunty
Teaching and Learning Inquiry.2026; 14: 1. CrossRef
Unraveling the connection: A systematic review of learning styles and mathematics achievement
Dewi D. Hariri, Hilda Mahmudah, Fayza S. Wibawa, Nia Kania
Pedagogical Research.2025; 10(1): em0232. CrossRef

Factors affecting nursing and health technician students' satisfaction with distance learning during the COVID-19 pandemic in Morocco: a descriptive study: Aziz Naciri, Mohamed Radid, Abderrahmane Achbani, Mohamed Amine Baba, Ahmed Kharbach, Ghizlane Chemsi; J Educ Eval Health Prof. 2022;19:28. Published online October 17, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.28

7,786 View
297 Download
3 Web of Science
3 Crossref

Abstract PDF Supplementary Material

Purpose
Distance learning describes any learning based on the use of new multimedia technologies and the internet to allow students to acquire new knowledge and skills at a distance. This study aimed to determine satisfaction levels with distance learning and associated factors among nursing and health technician students during the coronavirus disease 2019 pandemic in Morocco.
Methods
An descriptive study was conducted between April and June 2022 among nursing and health technician students using a self-administered instrument. The student satisfaction questionnaire consists of 24 questions categorized into 6 subscales: instructor, technology, course setup, interaction, outcomes, and overall satisfaction. It was based on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Univariate and multivariate logistic regression analyses were conducted to identify factors associated with student satisfaction during distance learning.
Results
A total of 330 students participated in this study, and 176 students (53.3%) were satisfied with the distance learning activities. A mean score higher than 2.8 out of 5 was obtained for all subscales. Multiple regression analysis showed that students’ year of study (adjusted odds ratio [aOR], 2.34; 95% confidence interval [CI], 1.28–4.27) and internet quality (aOR, 0.47; 95% CI, 0.29–0.77) were the significant factors associated with students’ satisfaction during distance learning.
Conclusion
This study highlights the satisfaction level of students and factors that influenced it during distance learning. A thorough understanding of student satisfaction with digital environments will contribute to the successful implementation of distance learning devices in nursing.

Citations

Citations to this article as recorded by

Factors associated with nursing and midwifery students’ satisfaction with the clinical learning environment: a cross-sectional study in Morocco
Khadija Guejdad, Laila Lahlou, Ali Ikrou, Redouane Abouqal, Jihane Belayachi
BMC Nursing.2025;[Epub] CrossRef
Influential Factors on Postgraduates’ Satisfaction with the Quality of Pharmacy Education: Evidence from a University in Vietnam
Do Xuan Thang, Nguyen Viet Hung, Vu Tran Anh, Vu Thi Quynh Mai, Le Thu Thuy, Cuc Thi Thu Nguyen, Trung Nguyen Duc, Dang Viet Hung
Pharmacy.2025; 13(3): 62. CrossRef
Satisfaction with online education among students, faculty, and parents before and after the COVID-19 outbreak: Evidence from a meta-analysis
Tianyuan Xu, Ling Xue
Frontiers in Psychology.2023;[Epub] CrossRef

Acceptability of the 8-case objective structured clinical examination of medical students in Korea using generalizability theory: a reliability study: Song Yi Park, Sang-Hwa Lee, Min-Jeong Kim, Ki-Hwan Ji, Ji Ho Ryu; J Educ Eval Health Prof. 2022;19:26. Published online September 8, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.26

6,780 View
258 Download
2 Web of Science
3 Crossref

Abstract PDF Supplementary Material

Purpose
This study investigated whether the reliability was acceptable when the number of cases in the objective structured clinical examination (OSCE) decreased from 12 to 8 using generalizability theory (GT).
Methods
This psychometric study analyzed the OSCE data of 439 fourth-year medical students conducted in the Busan and Gyeongnam areas of South Korea from July 12 to 15, 2021. The generalizability study (G-study) considered 3 facets—students (p), cases (c), and items (i)—and designed the analysis as p×(i:c) due to items being nested in a case. The acceptable generalizability (G) coefficient was set to 0.70. The G-study and decision study (D-study) were performed using G String IV ver. 6.3.8 (Papawork, Hamilton, ON, Canada).
Results
All G coefficients except for July 14 (0.69) were above 0.70. The major sources of variance components (VCs) were items nested in cases (i:c), from 51.34% to 57.70%, and residual error (pi:c), from 39.55% to 43.26%. The proportion of VCs in cases was negligible, ranging from 0% to 2.03%.
Conclusion
The case numbers decreased in the 2021 Busan and Gyeongnam OSCE. However, the reliability was acceptable. In the D-study, reliability was maintained at 0.70 or higher if there were more than 21 items/case in 8 cases and more than 18 items/case in 9 cases. However, according to the G-study, increasing the number of items nested in cases rather than the number of cases could further improve reliability. The consortium needs to maintain a case bank with various items to implement a reliable blueprinting combination for the OSCE.

Citations

Citations to this article as recorded by

From Agents to Governance: Essential AI Skills for Clinicians in the Large Language Model Era
Weiping Cao, Qing Zhang, Jialin Liu, Siru Liu
Journal of Medical Internet Research.2026; 28: e86550. CrossRef
Applying borderline methods for setting standards in objective structured clinical examination: the first case in undergraduate medical program in Vietnam
Hoa Thi Thu Doan, Hanh Thi My Nguyen, Anh Tuan Pham
MedPharmRes.2026; 10(1): 96. CrossRef
Applying the Generalizability Theory to Identify the Sources of Validity Evidence for the Quality of Communication Questionnaire
Flávia Del Castanhel, Fernanda R. Fonseca, Luciana Bonnassis Burg, Leonardo Maia Nogueira, Getúlio Rodrigues de Oliveira Filho, Suely Grosseman
American Journal of Hospice and Palliative Medicine®.2024; 41(7): 792. CrossRef

Medical students’ self-assessed efficacy and satisfaction with training on endotracheal intubation and central venous catheterization with smart glasses in Taiwan: a non-equivalent control-group pre- and post-test study: Yu-Fan Lin, Chien-Ying Wang, Yen-Hsun Huang, Sheng-Min Lin, Ying-Ying Yang; J Educ Eval Health Prof. 2022;19:25. Published online September 2, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.25

7,222 View
301 Download
2 Web of Science
3 Crossref

Abstract PDF Supplementary Material

Purpose
Endotracheal intubation and central venous catheterization are essential procedures in clinical practice. Simulation-based technology such as smart glasses has been used to facilitate medical students’ training on these procedures. We investigated medical students’ self-assessed efficacy and satisfaction regarding the practice and training of these procedures with smart glasses in Taiwan.
Methods
This observational study enrolled 145 medical students in the 5th and 6th years participating in clerkships at Taipei Veterans General Hospital between October 2020 and December 2021. Students were divided into the smart glasses or the control group and received training at a workshop. The primary outcomes included students’ pre- and post-intervention scores for self-assessed efficacy and satisfaction with the training tool, instructor’s teaching, and the workshop.
Results
The pre-intervention scores for self-assessed efficacy of 5th- and 6th-year medical students in endotracheal intubation and central venous catheterization procedures showed no significant difference. The post-intervention score of self-assessed efficacy in the smart glasses group was better than that of the control group. Moreover, 6th-year medical students in the smart glasses group showed higher satisfaction with the training tool, instructor’s teaching, and workshop than those in the control group.
Conclusion
Smart glasses served as a suitable simulation tool for endotracheal intubation and central venous catheterization procedures training in medical students. Medical students practicing with smart glasses showed improved self-assessed efficacy and higher satisfaction with training, especially for procedural steps in a space-limited field. Simulation training on procedural skills with smart glasses in 5th-year medical students may be adjusted to improve their satisfaction.

Citations

Citations to this article as recorded by

A narrative review of advancing medical education through technology: the role of smart glasses in situated learning
Bahareh Ghavami Hosein Pour, Zahra Karimian, Nazanin Hatami Niya
BMC Medical Education.2025;[Epub] CrossRef
Improvement of the Endotracheal Intubation Skill of Nurse Anesthesia Students Using Visual Self-evaluation in Iran: A Randomized Controlled Study
Mahdieh Parhizkar, Ali Khalafi, Masoumeh Albooghobeish, Nooshin Sarvi-Sarmeydani
Shiraz E-Medical Journal.2024;[Epub] CrossRef
The use of smart glasses in nursing education: A scoping review
Charlotte Romare, Lisa Skär
Nurse Education in Practice.2023; 73: 103824. CrossRef

Effect of a forensic nursing virtual education course on knowledge and clinical decision-making of master’s nursing students in Iran: a non-equivalent control group pre- and post-test study: Zeynab Firuzi, Mitra Sedghi Sabet, Fateme Jafaraghaee, Hedayat Jafari, Ehsan Kazemnezhad Leyli, Samad Karkhah, Mohammad Javad Ghazanfari; J Educ Eval Health Prof. 2022;19:20. Published online August 25, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.20

6,339 View
365 Download
5 Web of Science
6 Crossref

Abstract PDF Supplementary Material

Purpose
Forensic nursing is a specialty in the nursing profession based on legal procedures. This study aimed to assess the effect of a forensic nursing virtual education course on knowledge and clinical decision-making among master’s nursing students.
Methods
In a quasi-experimental study with a pre- and post-test, 106 master’s nursing students at Guilan (n=65) and Mazandaran (n=41) Universities of Medical Sciences, Iran were enrolled. Data were collected using census sampling from March to April 2021. Participants in the intervention group received a forensic nursing virtual education course in three 90-minute sessions for 2 days.
Results
A total of 88 out of 106 master’s nursing students were enrolled in this study. The mean post-education score for knowledge in the intervention group was significantly higher than in the control group (12.52 vs. 7.67, P<0.001). The mean post-education score for clinical decision-making in the intervention group was significantly higher than in the control group (16.96 vs. 13.64, P<0.001).
Conclusion
The level of knowledge and clinical decision-making of master’s nursing students regarding forensic evidence improved after the forensic nursing virtual education course in the intervention group compared to the control group. Nursing managers and policymakers can develop appropriate strategies to improve the knowledge and clinical decision-making of nursing students by using forensic nursing education courses in the curricula of nursing programs, especially in postgraduate education as an elective or mandatory course.

Citations

Citations to this article as recorded by

Development and Psychometric Evaluation of the Forensic Nursing Competency Scale‐Short Form for Hospital Nurses
Youn‐Jung Son, Na Young Jo, Yun Mi Lee, So Hyun Park
Journal of Advanced Nursing.2026; 82(6): 5937. CrossRef
Assessment of Primary Health Care Nurses’ Knowledge of Forensic Nursing Competencies
Jhuliano Silva Ramos de Souza, Zélia Marilda Rodrigues Resck, Rogério Silva Lima, Fábio de Souza Terra, Thiago Augusto Soares Monteiro da Silva, Taciana Silveira Passos, Sueli de Carvalho Vilela
Revista Brasileira de Enfermagem.2026;[Epub] CrossRef
Avaliação do Conhecimento dos Enfermeiros da Atenção Primária à Saúde sobre Competências em Enfermagem Forense
Jhuliano Silva Ramos de Souza, Zélia Marilda Rodrigues Resck, Rogério Silva Lima, Fábio de Souza Terra, Thiago Augusto Soares Monteiro da Silva, Taciana Silveira Passos, Sueli de Carvalho Vilela
Revista Brasileira de Enfermagem.2026;[Epub] CrossRef
Educational components related to the forensic nursing process: a scoping review
I Made Moh. Yanuar Saifudin, Wawan Febri Ramdani, Maryadi Maryadi
Egyptian Journal of Forensic Sciences.2026;[Epub] CrossRef
Improving English Pronunciation among Digital Natives through AI-Based Learning Tools
Tiara Noviarini, Galuh Yuliar Dinata
JPI (Jurnal Pendidikan Indonesia).2025; 14(4): 902. CrossRef
Knowledge and attitude of university nursing students towards forensic nursing and their influencing factors: a mixed-methods study
Ebtsam Aly Abou Hashish
Egyptian Journal of Forensic Sciences.2024;[Epub] CrossRef

Effect of online education on the knowledge on, attitudes towards, and skills in patient safety for nursing students in Korea: a mixed-methods study: Dan Bi Cho, Won Lee, So Yoon Kim, Sungkyoung Choi; J Educ Eval Health Prof. 2022;19:14. Published online June 30, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.14

8,280 View
462 Download
9 Web of Science
10 Crossref

Abstract PDF Supplementary Material

Purpose
The purpose of this study was to evaluate the impact of synchronous online education on the patient safety competency (knowledge, attitudes, and skills) of nursing students in Korea and to explore what they thought about this educational method.
Methods
A single-group pre-and post-test design and summative content analysis were implemented. On November 14th, 2020, 110 nursing students completed synchronous online patient safety education. Patient safety competency was measured before and after the intervention using the revised Patient Safety Competency Self-Evaluation tool. The descriptive statistics, paired t-test, and Wilcoxon signed-rank test were used to analyze the data. Students also expressed their opinions about this education based on open-ended questions.
Results
All the patient safety competency scores significantly increased after intervention. A summative content analysis of the open-ended questions yielded 5 categories: educational materials, education level, education time, interaction, and educational media.
Conclusion
This study found that synchronous online patient safety education improves nursing students’ knowledge on, attitudes towards, and skills in patient safety. Nursing students also expressed a variety of positive aspects of the online education method. To improve the efficacy of synchronous online patient safety education, there is a need for further empirical studies on the appropriate class duration and difficulty of the content. It is essential to find a way to combine online education with various learning activities.

Citations

Citations to this article as recorded by

Student Perspectives on the Efficacy of Hybrid Simulation Laboratory Teaching‐Practicing Modules
Jing Li, Xin Lin, Xiaoli Lian, Xiaodong Chen, Zhitao Wang, Chunxia Chen, Feifei Ma, Yao Chen, Yanmei Dai, Huiru Zou
European Journal of Dental Education.2026; 30(3): 945. CrossRef
Implementation and challenges of patient safety education for undergraduate nursing students: a scoping review
Xin Lu, Qingyi Xue, Xin Zhang, Cong Chen, Chunmei Zhang
BMC Nursing.2026;[Epub] CrossRef
Factors influencing patient safety competency in baccalaureate nursing students: A descriptive cross-sectional study
Shinae Ahn
Nurse Education Today.2025; 145: 106498. CrossRef
Factors influencing patient safety competence among Chinese vocational nursing students: A mixed-methods study using COM-B model and theoretical domains framework
Lu Zhou, Yinsong Sun, Mingzhao Xiao, Runli Yang, Shuang Zheng, Jun Shen, Qinghua Zhao
International Journal of Nursing Studies Advances.2025; 8: 100307. CrossRef
The Impact of a Flipped Learning on Nursing Students' Patient Safety Competencies and Satisfaction With the Education Method: A Randomized Controlled Trial
Vildan Budak, Hülya Fırat Kılıç, Seda Cevheroğlu
Nursing & Health Sciences.2025;[Epub] CrossRef
Effects of an integrated fall educational intervention in nursing students: a quasi-experimental study using virtual reality simulations
Sunghee H Tak, Dayeon Lee, Inyoung Seo, Hyein Choi, Eunbi Lee
Nurse Education in Practice.2025; : 104370. CrossRef
Effects of high-fidelity simulation and e-learning methods on nursing students’ self-efficacy in patient safety: a quasi-experimental study
Kevser Ozata, Leyla Dinc
BMC Nursing.2025;[Epub] CrossRef
Determining the self-evaluation levels of nursing students’ competency in child patient safety
Ahmet Butun, Kubra Ertekin
BMC Nursing.2025;[Epub] CrossRef
Effectiveness synchronous e-learning in nursing education: A meta-analysis and subgroup analysis
Songxian Jin, Jung-Hee Kim
Nurse Education in Practice.2024; 78: 104029. CrossRef
Effects of the small private online course combined with simulation-based training in a patient safety education program among nursing students: A quasi-experimental study
Hui Li, Wenzhong Zhao, Bo Li, Yanhong Li
International Journal of Nursing Sciences.2023; 10(4): 555. CrossRef

Brief report

Educational impact of an active learning session with 6-lead mobile electrocardiography on medical students’ knowledge of cardiovascular physiology during the COVID-19 pandemic in the United States: a survey-based observational study: Alexandra Camille Greb, Emma Altieri, Irene Masini, Emily Helena Frisch, Milton Leon Greenberg; J Educ Eval Health Prof. 2022;19:12. Published online June 20, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.12

6,467 View
271 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Mobile electrocardiogram (ECG) devices are valuable tools for teaching ECG interpretation. The primary purpose of this follow-up study was to determine if an ECG active learning session could be safely and effectively performed during the coronavirus disease 2019 (COVID-19) pandemic using a newly developed mobile 6-lead ECG device. Additionally, we examined the educational impact of these active learning sessions on student knowledge of cardiovascular physiology and the utility of the mobile 6-lead ECG device in a classroom setting. In this study, first-year medical students (MS1) performed four active learning activities using the new mobile 6-lead ECG device. Data were collected from 42 MS1s through a quantitative survey administered in September 2020. Overall, students felt the activity enhanced their understanding of the course material and that the activity was performed safely and in compliance with local COVID-19 guidelines. These results emphasize student preference for hands-on, small group learning activities in spite of the pandemic.

Citations

Citations to this article as recorded by

Medical student exam performance and perceptions of a COVID-19 pandemic-appropriate pre-clerkship medical physiology and pathophysiology curriculum
Melissa Chang, Andrew Cuyegkeng, Joseph A. Breuer, Arina Alexeeva, Abigail R. Archibald, Javier J. Lepe, Milton L. Greenberg
BMC Medical Education.2022;[Epub] CrossRef

Research article

No difference in factual or conceptual recall comprehension for tablet, laptop, and handwritten note-taking by medical students in the United States: a survey-based observational study: Warren Wiechmann, Robert Edwards, Cheyenne Low, Alisa Wray, Megan Boysen-Osborn, Shannon Toohey; J Educ Eval Health Prof. 2022;19:8. Published online April 26, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.8

24,287 View
849 Download
6 Web of Science
8 Crossref

Abstract PDF Supplementary Material

Purpose
Technological advances are changing how students approach learning. The traditional note-taking methods of longhand writing have been supplemented and replaced by tablets, smartphones, and laptop note-taking. It has been theorized that writing notes by hand requires more complex cognitive processes and may lead to better retention. However, few studies have investigated the use of tablet-based note-taking, which allows the incorporation of typing, drawing, highlights, and media. We therefore sought to confirm the hypothesis that tablet-based note-taking would lead to equivalent or better recall as compared to written note-taking.
Methods
We allocated 68 students into longhand, laptop, or tablet note-taking groups, and they watched and took notes on a presentation on which they were assessed for factual and conceptual recall. A second short distractor video was shown, followed by a 30-minute assessment at the University of California, Irvine campus, over a single day period in August 2018. Notes were analyzed for content, supplemental drawings, and other media sources.
Results
No significant difference was found in the factual or conceptual recall scores for tablet, laptop, and handwritten note-taking (P=0.61). The median word count was 131.5 for tablets, 121.0 for handwriting, and 297.0 for laptops (P=0.01). The tablet group had the highest presence of drawing, highlighting, and other media/tools.
Conclusion
In light of conflicting research regarding the best note-taking method, our study showed that longhand note-taking is not superior to tablet or laptop note-taking. This suggests students should be encouraged to pick the note-taking method that appeals most to them. In the future, traditional note-taking may be replaced or supplemented with digital technologies that provide similar efficacy with more convenience.

Citations

Citations to this article as recorded by

The Power of the Pen in the Digital Age: A Study on the Note-Taking Preferences of Medical School Students
Ahmet Öksüz, Özlem Coşkun
STED / Sürekli Tıp Eğitimi Dergisi.2026; 35(2): 117. CrossRef
Scrittura a mano all'Università
Arianna Beri
QUADERNI DI DIDATTICA DELLA SCRITTURA.2026; (43): 141. CrossRef
Digital Note-Taking and Academic Performance of Nursing Students of University of Perpetual Help System- DALTA Calamba Campus
Nica Rose Desate, Ma. Samantha Mickaela Huertas, Jem Rowie Piamonte
QualiSearch Journal of Educational Research and Practice.2026;[Epub] CrossRef
Evaluating the Use of a Note-Taking App by Japanese Resident Physicians: Nationwide Cross-Sectional Study
Taiju Miyagami, Yuji Nishizaki, Taro Shimizu, Yu Yamamoto, Kiyoshi Shikino, Koshi Kataoka, Masanori Nojima, Gautam A Deshpande, Toshio Naito, Yasuharu Tokuda
JMIR Formative Research.2025; 9: e55087. CrossRef
Cognitive-digital interaction: the state of the field, weaknesses and solutions
Anastasia Anufrieva
Acta Psychologica.2025; 259: 105229. CrossRef
A Classroom Study on Notetaking Modalities and Inattentive Attention‐Deficit/Hyperactivity Disorder Symptoms
Gabrielle A. Shimko, Emily R. Fyfe, Karin H. James
Applied Cognitive Psychology.2025;[Epub] CrossRef
Exploring the impact of note taking methods on cognitive function among university students
Alham Al-Sharman, Reime Jamal Shalash, Taif A. M. Omran, Rofaida Mohamed Elsayed, Ilhan Abdi Warfa, Wala Siddig Elsayed Ali Adawi, Amna Obaid Aljaberi, Alia Abdulla Alabdooli, Ashokan Arumugam, Sivapriya Ramakrishnan, Nabil Saad, Amal Ahbouch, Wegdan Bani
BMC Medical Education.2025;[Epub] CrossRef
Typed Versus Handwritten Lecture Notes and College Student Achievement: A Meta-Analysis
Abraham E. Flanigan, Jordan Wheeler, Tiphaine Colliot, Junrong Lu, Kenneth A. Kiewra
Educational Psychology Review.2024;[Epub] CrossRef

Educational/Faculty training material

Online interprofessional education materials through a community learning program during the COVID 19 pandemic in Chile: Sandra Oyarzo Torres, Mónica Espinoza Barrios; J Educ Eval Health Prof. 2022;19:6. Published online March 24, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.6

8,644 View
309 Download
3 Web of Science
4 Crossref

Abstract PDF Supplementary Material

This article aims to share the online collaborative experience of interprofessional teamwork among healthcare undergraduate students based on community learning during the coronavirus disease 2019 (COVID-19) pandemic in Chile. This experience took place in 48 different communities in Chile from November 10, 2020 to January 12, 2021. It was a way of responding to the health education needs of the community when the entire Chilean population was in confinement. Students managed to adapt to the COVID-19 pandemic despite the challenges, including internet connectivity problems and the limited time available to do the work. The educational programs and videos shared in this article will be helpful for other interprofessional health educators to implement the same kind of program.

Citations

Citations to this article as recorded by

Interdisciplinary Vocational Training for Resilient, Integrated, and Person-Centered Health Systems
Mónica Valenzuela, Osvaldo Artaza, Javier Santa Cruz, Marcelo Fagalde, Mónica Espinoza, Sandra Oyarzo, Erich V. De Paula, Rogelio Pizzi
Archives of Medical Research.2025; 56(7): 103243. CrossRef
Social representations of interdisciplinary work from the perspectives of students and clinical tutors on a cardiovascular rehabilitation program in Chile
Raúl Ahumada, Marcela Aravena-Winkler, Cinara Sacomori
International Journal of Educational Research Open.2025; 9: 100515. CrossRef
A community-engaged interprofessional project led by medical students, school of social work students, and resident physicians: lessons learned and recommendations for success
Sabrina Orta, Daniela Santos Cantu, Giuiseppe Allan Fonseca, Luis Torres-Hostos, Chelsea Chang
Journal of Interprofessional Care.2024; 38(6): 1008. CrossRef
Successful Development and Implementation of a Large Virtual Interprofessional Education Activity Applying the Social Determinants of Health
Karl R. Kodweis, Elizabeth A. Hall, Chelsea P. Renfro, Neena Thomas-Gosain, Robin Lennon-Dearing, Jonathon K. Walker, Tyler M. Kiles
Pharmacy.2022; 10(6): 157. CrossRef

Review

The relationship of non-cognitive factors to academic and clinical performance in graduate rehabilitation science students in the United States: a systematic review: Kelly Reynolds, Caroline Bazemore, Cannon Hanebuth, Steph Hendren, Maggie Horn; J Educ Eval Health Prof. 2021;18:31. Published online November 23, 2021; DOI: https://doi.org/10.3352/jeehp.2021.18.31

12,251 View
384 Download
15 Web of Science
21 Crossref

Abstract PDF Supplementary Material

Purpose
Rehabilitation science programs utilize cognitive and non-cognitive factors to select students who can complete the didactic and clinical portions of the program and pass the licensure exam. Cognitive factors such a prior grade point average and standardized test scores are known to be predictive of academic performance, but the relationship of non-cognitive factors and performance is less clear. The purpose of this systematic review was to explore the relationship of non-cognitive factors to academic and clinical performance in rehabilitation science programs.
Methods
A search of 7 databases was conducted using the following eligibility criteria: graduate programs in physical therapy (PT), occupational therapy, speech-language pathology, United States-based programs, measurement of at least 1 non-cognitive factor, measurement of academic and/or clinical performance, and quantitative reporting of results. Articles were screened by title, abstract, and full text, and data were extracted.
Results
After the comprehensive screening, 21 articles were included in the review. Seventy-six percent of studies occurred in PT students. Grit, self-efficacy, emotional intelligence, and stress were the most commonly studied factors. Only self-efficacy, emotional intelligence, and personality traits were examined in clinical and academic contexts. The results were mixed for all non-cognitive factors. Higher grit and self-efficacy tended to be associated with better performance, while stress was generally associated with worse outcomes.
Conclusion
No single non-cognitive factor was consistently related to clinical or academic performance in rehabilitation science students. There is insufficient evidence currently to recommend the evaluation of a specific non-cognitive factor for admissions decisions.

Citations

Citations to this article as recorded by

The Academic Threat Appraisal Ratio Scale (ATARS): Insights into attainment, academic progression, and retention in higher education
Simon Cassidy
British Journal of Educational Psychology.2026; 96(1): 90. CrossRef
Improving Equity Within the Holistic Admission Process Through Race-Neutral Holistic Review: A Before and After Cross-Sectional Study
Megan Eikenberry, Kylie Scott, Jaime González, Josh Subialka, Lacey Frankland, Patrice Ayala, Mark Kargela, Byron Russell
Journal of Physical Therapy Education.2026; 40(1): 32. CrossRef
Individual differences and self-regulatory factors are credible determinants of physiotherapy student performance on clinical placement: Insights from a measurement burst design study
Alan Reubenson, Hugh Riddell, Margo L. Brewer, Leo Ng, Daniel F. Gucciardi
Advances in Health Sciences Education.2026; 31(2): 573. CrossRef
Exploring the role of experiential learning in shaping physical therapy student self-efficacy for neurologic practice: A mixed methods study
Martha D. Cullaty, Jenny Hall, David Halpin, Lisa Allison-Jones
Physiotherapy Theory and Practice.2026; : 1. CrossRef
Understanding the Struggles of Stem Students in Stem Education
Feme Hardi May Respecia, Earlynn Lourence A. Tumampos, Kaiya Angela C. Manguilimotan, John Dexter S. Majadas, Princess Althea M. Asis, Shiara Collin P. De Vera, Lourian Joy B. Barrios, Elijah Niobe T. Omaña, Kenneth M. Juntilla, JOSHUA B. AMPER, Mariel M.
Sprin Journal of Arts, Humanities and Social Sciences.2026; 4(12): 1. CrossRef
Non-Academic Predictors of Academic Performance Throughout a Physical Therapist Education Program
Kelly Reynolds, Maggie Horn, Karen Huhn, Steven George
Journal of Allied Health.2026; 55(1): 21. CrossRef
Relationship between non-cognitive skills and academic performance in Japanese physical therapy students: an exploratory study
Kazuto Yamaguchi, Fumiya Inagaki
Journal of Physical Therapy Science.2026; 38(5): 211. CrossRef
Predicting performance in a doctor of physical therapy gross anatomy course based on an exploratory factor analysis of the anatomical self‐efficacy instrument
Kelly Reynolds, Shani Mueller, Maggie Horn
Anatomical Sciences Education.2025; 18(8): 815. CrossRef
The Relationship between Cognitive Factors and Noncognitive Factors, Including Grit, and NBCOT® Exam Performance
Anne H. Zachry, Stephanie Lancaster, Amy Hall, April Hilsdon
Occupational Therapy In Health Care.2025; 39(3): 558. CrossRef
Academic Coaching in Entry-level Doctor of Physical Therapy Education
Kayla Black, Jessica Feda, Breanna Reynolds, Guillermo Cutrone, Kendra Gagnon
Journal of Physical Therapy Education.2025; 39(2): 125. CrossRef
Examining the Mediating Role of Grit and Self‐Efficacy in the Association Between Growth Mindset and Job Satisfaction in a Sample of Chinese Nurses
Xiaoyan Zhang, Chen Qiu, Xiaolin Li, Aniruddha Shekara, Xueling Suo, Song Wang, De-Chih Lee
Journal of Nursing Management.2025;[Epub] CrossRef
Non-Cognitive Factors Influencing the Development of Nursing Students’ Clinical Judgment
Kathryn L. Robinson, Barbara Jackson, Nancy York, Lyndsey K. Blair
Nurse Educator.2025; 50(5): 272. CrossRef
Exploring Scientific Reasoning and Genetics Self-efficacy as Predictors of Secondary School Students’ Achievement in Genetics
Ayodeji Temitope Ojo, Temisan A. Ige
African Journal of Research in Mathematics, Science and Technology Education.2025; 29(2): 252. CrossRef
Gut Microbiota and Academic Traits: A Global Bibliometric Perspective
Xinjie Zhang, Jianqiang Ye
X-Disciplinarity.2025; 1(3): 4. CrossRef
Predictors of Learning Self‐Efficacy Among Nursing Students in Palestine
Ashraf Jehad Abuejheisheh, Rabia H. Haddad, Salam Bani Hani, Mustafa Rabea, Jadallah Afaneh, Khalid Shqirat, Ahmad Maree, Seraj Ayyad, Mohammad Abu Mariah, Abdullah Thaiabeh, Mohammad Basheer, Foroozan Atashzadeh-Shoorideh
Nursing Forum.2025;[Epub] CrossRef
Basic components of a personal resource determining a personified subject of activity: The impact on university students’ academic achievement
Vera Stepanovna Tretyakova, Anastasia Evgenievna Kaigorodova, Anton Alexandrovich Sharov, Evald Friedrichovich Zeer
Science for Education Today.2024; 14(2): 206. CrossRef
Comparison of 2 Methods of Debriefing for Learning of Interprofessional Handoff Skills
Julie Ronnebaum, Chunfa Jie, Kristina Salazar
Journal of Acute Care Physical Therapy.2023; 14(1): 18. CrossRef
Investigating Latent Interactions between Students’ Affective Cognition and Learning Performance: Meta-Analysis of Affective and Cognitive Factors
Jian Li, Eryong Xue, Chenchang Li, Yunshu He
Behavioral Sciences.2023; 13(7): 555. CrossRef
Relationship between digital capabilities and academic performance: the mediating effect of self-efficacy
Rasha Kadri Ibrahim, Aisha Namshan Aldawsari
BMC Nursing.2023;[Epub] CrossRef
The study of the personification of the actor as a psychological phenomenon
Vera Stepanovna Tretyakova, Anastasia Evgenievna Kaigorodova, Anton Alexandrovich Sharov, Evald Friedrichovich Zeer
Science for Education Today.2023; 13(3): 101. CrossRef
Grouping of academic achievements’ predictors in Russian current psychological and pedagogical studies
Roman A. Dormidontov
Psychological-Pedagogical Journal GAUDEAMUS.2022; (4): 18. CrossRef

First
Prev
Page of 3
Next
Last

Search