Effect of ChatGPTon Various Aspects of Undergraduate Medical Education
DOI:
https://doi.org/10.52206/jsmc.2025.15.2.997Abstract
Background: Artificial intelligence (AI) has emerged as a transformative tool across various domains, including medical education. AI applications, such as ChatGPT, present new opportunities for enhancing student learning, instructional strategies, clinical scenario simulations, and collaborative learning.
Objective: This study examines the impact of Artificial Intelligence on various aspects of undergraduate medical education.
Materials and Methods: This study employed a quantitative cross-sectional design to assess the impact of AI in medical education. A total of 300 participants, including medical students, educators, and healthcare professionals, were surveyed. The study was conducted over six months (June to December 2023) across multiple medical institutions. Scenario modeling was used to simulate clinical cases, where participants engaged in AI-assisted diagnostic decision-making and learning activities in a controlled academic setting. Ethical approval was obtained from the Institutional Review Board (IRB) of Women Medical & Dental College Abbottabad. Data were collected using a structured questionnaire, which evaluated AI's role in student learning, collaborative education, and instructional methods. Factor analysis and Cronbach's alpha were used to validate the questionnaire's reliability and construct validity.
Results: It revealed significant positive impacts, with 80% showed improved learning experiences and 85% acknowledging the benefits of simulated clinical scenarios. Collaborative learning effectiveness was recognized by 70%, mainly among educators who noted adaptability. Reliability measures showed strong internal consistency (Cronbach's alpha values), and factor analysis highlighted key dimensions such as Personalized Learning.
Conclusion: This research provides empirical evidence in support of using AI in medical education. The results indicate that AI applications can improve student learning outcomes, enhance teaching, and even assist in the simulation of clinical scenarios. These finding further indicate research directed towards optimising advanced technology in medical education.
Keywords: Artificial intelligence (AI), AI in healthcare, Clinical simulations, Collaborative learning, Medical education, Student learning.
References
Preiksaitis C, Rose C. Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review. JMIR medical education. 2023:9;e48785. https://doi.org/10.2196/48785
Grunhut J, Marques O, Wyatt AT. Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR medical education. 2022:8(2); e35587. https://doi.org/10.2196/35587
Charow R. Artificial intelligence education programs for health care professionals: scoping review. JMIR Medical Education. 2021:7(4); e31043. https://doi.org/10.2196/31043
Mir MM. Application of artificial intelligence in medical education: current scenario and future perspectives. Journal of advances in medical education & professionalism. 2023:11(3); 133. PMC10352669
Oyeniyi J, Oluwaseyi P. Emerging trends in AI- powered medical imaging: enhancing diagnostic accuracy and treatment decisions. International Journal of Enhanced Research In Science Technology & Engineering. 2024:13; 2319-7463. ISSN: 2319-7463
Lee H. The rise of ChatGPT: Exploring its potential in medical education. Anatomical sciences education. 2024:17(5);926-31. https://doi.org/10.1002/ase.2270
Magalhães SA, Cruz-Correia R. Incorporating ChatGPT in medical informatics education: mixed methods study on student perceptions and experiential integration proposals. JMIR medical education. 2024:10; e51151. https://doi.org/10.2196/51151
Alkhaaldi SM. Medical student experiences and perceptions of ChatGPT and artificial intelligence: cross-sectional study. JMIR Medical Education. 2023:9(1); e51302. https://doi.org/10.2196/51302
Sami A. Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility. BMC Medical Education. 2025:25(1);82. https://doi.org/10.1186/s12909-025-06704-y
Salloum A, Alfaisal R, Salloum SA. Revolutionizing medical education: empowering learning with ChatGPT, in Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom. 2024:Springer;79-90. https://doi.org/10.1007/978-3-031-52280-2_6
Wu Y. Embracing ChatGPT for medical education: exploring its impact on doctors and medical students. JMIR Medical Education. 2024:10; e52483. https://doi.org/10.2196/52483
Chun J. A Comparative Analysis of On-Device AI- Driven, Self-Regulated Learning and Traditional Pedagogy in University Health Sciences Education. Applied Sciences. 2025:15(4);1815. https://doi.org/10.3390/app15041815
Galdames IS. From Anatomy to Algorithm: Scope of AI-Assisted Diagnostic Competencies in Health Sciences Education. International Journal of Medical and Surgical Sciences,(IJMSS). 2024: 11(3);1-24. ISSN 0719-3904
Weidener L, Fischer M. Proposing a principle-based approach for teaching AI ethics in medical education. JMIR Medical Education. 2024:10(1); e55368. https://doi.org/10.2196/55368
Sabri H. Performance of three artificial intelligence (AI)-based large language models in standardized testing; implications for AI-assisted dental education. Journal of periodontal research. 2024. https://doi.org/10.1111/jre.13323
Kung TH. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS digital health. 2023:2(2);e0000198. https://doi.org/10.1371/journal.pdig.0000198
Sharma P. Performance of ChatGPT on USMLE: Unlocking the potential of large language models for AI-assisted medical education. arXiv preprint arXiv:2307.00112, 2023. https://doi.org/10.48550/arXiv.2307.00112
Tangadulrat P, Sono S, Tangtrakulwanich B. Using ChatGPT for clinical practice and medical education: cross-sectional survey of medical students' and physicians' perceptions. JMIR Medical Education. 2023:9(1);e50658. https://doi.org/10.2196/50658
Chaiban T. The intent of ChatGPT usage and its robustness in medical proficiency exams: a systematic review. Discover Education. 2024:3(1);232. https://doi.org/10.1007/s44217-024-00332-2
Menon D, Shilpa K. “Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon. 2023:9(11). https://doi.org/10.1016/j.heliyon.2023.e20962
Wongras P, Tanantong T. An extended UTAUT model for analyzing users' Acceptance factors for artificial Intelligence adoption in human resource recruitment: A case study of Thailand. Education and Information Technologies. 2023:3(7);13-27.
Khan M. Integration of ChatGPT in IMG Study Strategies: An Exploratory Study of User Experiences and Perceptions. Innovative Research in Applied, Biological and Chemical Sciences. 2024:2(1):47-54. https://doi.org/10.62497/IRABCS.2024.29
Firat M. What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching. 2023:6(1);57-63. https://doi.org/10.37074/jalt.2023.6.1.22
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