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]

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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.

Performance of large language models on Thailand’s national medical licensing examination: a cross-sectional study: Prut Saowaprut, Romen Samuel Wabina, Junwei Yang, Lertboon Siriwat; J Educ Eval Health Prof. 2025;22:16. Published online May 12, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.16

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Purpose
This study aimed to evaluate the feasibility of general-purpose large language models (LLMs) in addressing inequities in medical licensure exam preparation for Thailand’s National Medical Licensing Examination (ThaiNLE), which currently lacks standardized public study materials.
Methods
We assessed 4 multi-modal LLMs (GPT-4, Claude 3 Opus, Gemini 1.0/1.5 Pro) using a 304-question ThaiNLE Step 1 mock examination (10.2% image-based), applying deterministic API configurations and 5 inference repetitions per model. Performance was measured via micro- and macro-accuracy metrics compared against historical passing thresholds.
Results
All models exceeded passing scores, with GPT-4 achieving the highest accuracy (88.9%; 95% confidence interval, 88.7–89.1), surpassing Thailand’s national average by more than 2 standard deviations. Claude 3.5 Sonnet (80.1%) and Gemini 1.5 Pro (72.8%) followed hierarchically. Models demonstrated robustness across 17 of 20 medical domains, but variability was noted in genetics (74.0%) and cardiovascular topics (58.3%). While models demonstrated proficiency with images (Gemini 1.0 Pro: +9.9% vs. text), text-only accuracy remained superior (GPT-4o: 90.0% vs. 82.6%).
Conclusion
General-purpose LLMs show promise as equitable preparatory tools for ThaiNLE Step 1. However, domain-specific knowledge gaps and inconsistent multi-modal integration warrant refinement before clinical deployment.

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Is artificial intelligence getting better at anatomy? A two‐year review of ChatGPT's free public versions
Bahattin Paslı, Ceren Günenç Beşer
Anatomical Sciences Education.2026; 19(8): 1279. CrossRef
The performance of ChatGPT and other large language models on multiple‐choice questions in biomedical disciplines: A meta‐analysis
Colleen M. Cheverko, Volodymyr Mavrych, Olena Bolgova, Fathima Raahima Riyas Mohamed, Jennifer Westrick, Lorena Juarez, Emily Rush, Kathryn A. Solka, Alison F. Doubleday, Jessica N. Byram, Robert Becker, Victoria Gomez, Brenda K. Anak Ganeng, Leslie A. Ho
Anatomical Sciences Education.2026;[Epub] CrossRef
Performance of GPT-4o and o1-Pro on United Kingdom Medical Licensing Assessment-style items: a comparative study
Behrad Vakili, Aadam Ahmad, Mahsa Zolfaghari
Journal of Educational Evaluation for Health Professions.2025; 22: 30. CrossRef
Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study
Toshimune Ito, Toru Ishibashi, Tatsuya Hayashi, Shinya Kojima, Kazumi Sogabe
JMIR Medical Education.2025; 11: e81807. 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

The effect of strengthening nurse practitioners’ competency in occupational health services for agricultural workers exposed to pesticides in primary care units, Thailand: a before-and-after study: Napamon Pumsopa, Ann Jirapongsuwan, Surintorn Kalampakorn, Sukhontha Siri; J Educ Eval Health Prof. 2025;22:14. Published online April 21, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.14

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Abstract PDF Supplementary Material

Purpose
This study aimed to evaluate the effect of the Strengthening Nurse Practitioners’ Competency in Occupational Health Service (SNPCOHS) program. It was hypothesized that nurse practitioners (NPs) participating in the program would demonstrate increased competency in providing occupational health services to agricultural workers exposed to pesticides in primary care units (PCUs) compared to their baseline competency and to a comparison group.
Methods
A quasi-experimental study was conducted between August and December 2023. The 4-week intervention included 5 hours of an e-learning program, 3 hours of online discussion, and 2 hours dedicated to completing an assignment. The program was evaluated at 3 time points: pre-intervention, post-intervention (week 4), and follow-up (week 8). Sixty NPs volunteered to participate, with 30 in the experimental group and 30 in the comparison group. Data on demographics, professional attributes, knowledge, skills, and perceived self-efficacy were collected using self-administered questionnaires via Google Forms. Data analysis involved descriptive statistics, independent t-tests, and repeated measures analysis of variance.
Results
The experimental group demonstrated significantly higher mean scores in professional attributes, knowledge, skills, and perceived self-efficacy in providing occupational health services to agricultural workers exposed to pesticides compared to the comparison group at both week 4 and week 8 post-intervention.
Conclusion
The SNPCOHS program is well-suited for self-directed learning for nurses in PCUs, supporting effective occupational health service delivery. It should be disseminated and supported as an e-learning resource for NPs in PCUs (Thai Clinical Trials Registry: TCTR20250115004).

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Nurse practitioners’ competencies for occupational health services in primary care units, Thailand: a qualitative study
Napamon Pumsopa, Ann Jirapongsuwan, Surintorn Kalampakorn, Sukhontha Siri
Scientific Reports.2026;[Epub] CrossRef

Research Article

Learning styles and academic achievement among undergraduate medical students in Thailand: Wichuda Jiraporncharoen, Chaisiri Angkurawaranon, Manoch Chockjamsai, Athavudh Deesomchok, Juntima Euathrongchit; J Educ Eval Health Prof. 2015;12:38. Published online July 8, 2015; DOI: https://doi.org/10.3352/jeehp.2015.12.38

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Abstract PDF

Purpose
This study aimed to explore the associations between learning styles and high academic achievement and to ascertain whether the factors associated with high academic achievement differed between preclinical and clinical students. Methods: A survey was conducted among undergraduate medical students in Chiang Mai University, Thailand. The Index of Learning Styles questionnaire was used to assess each student’s learning style across four domains. High academic achievement was defined as a grade point average of at least 3.0. Results: Of the 1,248 eligible medical students, 1,014 (81.3%) participated. Learning styles differed between the preclinical and clinical students in the active/reflective domain. A sequential learning style was associated with high academic achievement in both preclinical and clinical students. A reflective learning style was only associated with high academic achievement among preclinical students. Conclusion: The association between learning styles and academic achievement may have differed between preclinical and clinical students due to different learning content and teaching methods. Students should be encouraged to be flexible in their own learning styles in order to engage successfully with various and changing teaching methods across the curriculum. Instructors should be also encouraged to provide a variety of teaching materials and resources to suit different learning styles.

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Ann George, K Chandrahasan, Senkadhirdasan Dakshinamurthy
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Liudmyla V. Hapon-Baida, Tetiana M. Derkach
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Haroula Tzamaras, Dailen Brown, Jason Moore, Scarlett R. Miller
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