Students’ Perspectives and Experiences on Artificial Intelligence in Health Professions Education: A Qualitative Study

Document Type : Original Article

Authors

1 Student Research Committee, Zanjan University of Medical Sciences, Zanjan, Iran

2 Department of Medical Education, Health Professions Education Research Center, Education Development Center, Tehran University of Medical Sciences, Tehran, Iran

3 Student Committee for Education Development in Medical Sciences, Zanjan University of Medical Sciences, Zanjan, Iran

4 Department of Nutrition, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran

Abstract

Background: As artificial intelligence (AI) becomes more integrated into healthcare and education, the health sciences education literature is said to be sparse regarding how undergraduate health sciences students perceive AI in medical education. The purpose of this research is to explore the perceptions, emotional reactions, and experiences of undergraduate students in a variety of health disciplines on the use of AI in medical education.
Methods: A phenomenological study using semi-structured interviews was carried out with 16 students from seven health-related disciplines at Zanjan University of Medical Sciences. Data were analyzed by thematic analysis following the Braun and Clarke Framework.
Results: Five major themes and fifteen subthemes surfaced: 1) Conceptual Understanding and Cognitive Framing: students showed limited technical understanding and awareness of safety and ethical issues; 2) Emotional Landscape: students' emotions ranged from excitement to anxiety and ambiguity; 3) Patterns of Interaction: students frequently utilized AI tools for writing and learning, and there was clear evidence of ethical misuse in their responses; 4) Perceived Educational and Clinical Value: AI was seen by students as valuable when supporting research, supporting clinical decision making, and in telemedicine; 5) Ethical and Institutional Dimensions: these included loss of empathy, unclear boundaries of responsibility, and the need for formal curriculum integration.      
Conclusion: Students are eager to adopt AI, but lack formal knowledge of its ethical and clinical implications. Curricular reforms should incorporate AI literacy, critical appraisal, and safe practice guidelines. Tailored, interdisciplinary education is essential to prepare future health professionals to work responsibly with AI.

Keywords

Main Subjects

References

  1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
  2. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
  3. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-9.
  4. Orth M, Averina M, Chatzipanagiotou S, Faure G, Haushofer A, Kusec V, et al. Opinion: redefining the role of the physician in laboratory medicine in the context of emerging technologies, personalised medicine and patient autonomy ('4P medicine'). J Clin Pathol. 2019;72(3):191-7.
  5. Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med. 2020;7:27.
  6. Tajdini F, Kheiri M-J. A Review of Artificial Intelligence in Medicine. 2022.
  7. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
  8. Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26(6):900-8.
  9. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Acad Med. 2024;99(1):22-7.
  10. Zhang W, Cai M, Lee H, Evans R, Zhu C, Ming C. AI in Medical Education: Global situation, effects and challenges. Educ Inf Technol. 2023;29:1-23.
  11. Sriram A, Ramachandran K, Krishnamoorthy S. Artificial Intelligence in Medical Education: Transforming Learning and Practice. Cureus. 2025;17(3):e80852.
  12. Narayanan S, Ramakrishnan R, Durairaj E, Das A. Artificial Intelligence Revolutionizing the Field of Medical Education. Cureus. 2023;15(11):e49604.
  13. Bowers P, Graydon K, Ryan T, Lau JH, Tomlin D. Artificial intelligence-driven virtual patients for communication skill development in healthcare students: A scoping review. Australasian Journal of Educational Technology. 2024.
  14. Shah NR. Health Care in 2030: Will Artificial Intelligence Replace Physicians? Ann Intern Med. 2019;170(6):407-8.
  15. Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus. 2023;15(8):e43262.
  16. Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ. 2023;9:e48785.
  17. Ali M. The role of AI in reshaping medical education: opportunities and challenges. Clin Teach. 2025;22(2):e70040.
  18. Marques M, Almeida A, Pereira H. The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making. Cureus. 2024;16(9):e69405.
  19. Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, et al. Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR Med Educ. 2023;9:e48291.
  20. Sezgin E. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. Digit Health. 2023;9:20552076231186520.
  21. Guest G, Bunce A, Johnson L. How Many Interviews Are Enough? Field Methods - FIELD METHOD. 2006;18:59-82.
  22. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77-101.
  23. Amiri H, Peiravi S, rezazadeh shojaee Ss, Rouhparvarzamin M, Nateghi MN, Etemadi MH, et al. Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC med educ. 2024;24(1):412.
  24. Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Sci Rep. 2023;6(3):e1138.
  25. Plata S, De Guzman MA, Quesada A. Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education. 2023.
  26. Ortiz-Bonnin S, Blahopoulou J. Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students. Soc Psychol Educ. 2025;28(1):113.
  27. Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, Attitudes, and Perceptions of Artificial Intelligence in Healthcare Among Medical Students at Sultan Qaboos University. Cureus. 2023;15(9):e44887.
  28. Syeda LH, Batool Z, Hayder Z, Ali S. Medical undergraduate students’ awareness and perspectives on artificial intelligence: A developing nation’s context. BMC med educ. 2025;25(1):1060.
  29. Ahadiat S-A, Shirazinia M, Shirazinia S, Garousi S, Mottahedi M, Jalali AB, et al. Role of Telemedicine in Management of Patients During the COVID-19 Pandemic. Kindle. 2022;2(1):1-191.
  30. Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC med educ. 2023;23(1):852.

31.       Ma Y, Song Y, Balch JA, Ren Y, Vellanki D, Hu Z, et al. Promoting AI competencies for medical students: a scoping review on frameworks, programs, and tools. arXiv preprint arXiv:240718939. 2024.

Future of Medical Education Journal
Volume 15, Issue 3
Special Issue on Artificial Intelligence
September 2025
Pages 16-23

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