THE CLASH OF AUTHORITY IN DENTAL EDUCATION: A QUALITATIVE STUDY ON HOW PESHAWAR’S DENTAL RESIDENTS BALANCE AI RECOMMENDATIONS WITH CONSULTANT EXPERTISE

Main Article Content

Khadija Bibi
Syed Gohar Hussain Shah
Abdul Basit
Wasif Ali Shah
Muhammad Abbas Khan
Hanzala Waqar

Keywords

Health Professions Education; Medical Education; Artificial Intelligence; Hidden Curriculum; Professional Identity Formation; Workplace-Based Learning

Abstract

Background: Artificial Intelligence (AI) is increasingly integrated into health professions education, particularly within workplace-based clinical training environments. While AI-supported tools promote evidence-informed decision-making, they may also challenge traditional supervisory authority. These tensions represent important but underexplored educational challenges in postgraduate dental training.


Objective: To explore how dental residents in Peshawar experience and manage conflicts between AI-generated clinical recommendations and consultant expertise, and how these encounters influence learning, professional identity formation, and the hidden curriculum.


Methods: A qualitative phenomenological study was conducted between January and June 2024. Fourteen senior dental residents were recruited using purposive maximum variation sampling from public and private dental institutions. Semi-structured interviews focused on critical learning incidents involving authority conflict. Data were analyzed using thematic analysis.


Results: Four major themes emerged: (1) clinical dissonance as a learning tension, (2) hierarchy, assessment, and silence in workplace-based learning, (3) institutional culture shaping epistemic authority, and (4) professional identity formation between obedience and evidence. Residents described emotional discomfort, fear of evaluation, and gradual shaping of professional identity through implicit institutional norms.


Conclusion: Conflicts between algorithmic recommendations and consultant authority are educational phenomena with significant implications for health professions education. Addressing epistemic authority, psychological safety, and algorithmic humility is essential for preparing reflective, evidence-informed clinicians.

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References

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7
2. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. Med Educ. 2019;53(4):339–47. doi:10.1111/medu.13764
3. Wartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018;93(8):1107–9. doi:10.1097/ACM.0000000000002044
4. Masters K. Ethical use of artificial intelligence in health professions education. Med Teach. 2023;45(5):1–7. doi:10.1080/0142159X.2023.2186203
5. Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum frameworks and educational programs in AI for medical students, residents, and physicians: a scoping review. JMIR Med Educ. 2024;10:e54793. doi:10.2196/54793
6. Charow R, Jeyakumar T, Younus S, et al. Artificial intelligence education programs for clinicians: a scoping review. NPJ Digit Med. 2021;4:18. doi:10.1038/s41746-020-00376-4
7. McCoy LG, Nagaraj S, Morgado F, et al. What do medical students think about artificial intelligence? A qualitative study. BMC Med Educ. 2020;20:1–10. doi:10.1186/s12909-020-02040-2
8. Ali S, Khan M, Abbas S, et al. Medical students’ perceptions toward artificial intelligence in medical education and practice: a multinational study. BMC Med Educ. 2024;24:804. doi:10.1186/s12909-024-05804-3
9. Shaw K, Henning MA, Webster CS. Artificial intelligence in medical education: a scoping review of the literature. Med Sci Educ. 2024;34:223–37. doi:10.1007/s40670-023-01892-3
10. Sethi A. Artificial intelligence in health professions education. J Shalamar Med Dent Coll. 2024;5(1):1–9.
11. Ullah H, Shah S, Yousaf G. Effects of hidden curriculum on medical education and strategies to reframe it. Adv Med Educ Pract. 2024;15:101–12. doi:10.2147/AMEP.S437821
12. Shakerinejad M, Gholamnia Z, Nikpour M, Khodadadi E. Dental students’ experience of hidden curriculum: a qualitative study. J Med Educ Dev. 2024;17(54):31–43. No DOI assigned
13. Watling C, Lingard L. Toward meaningful evaluation of medical trainees: the influence of context. Med Educ. 2012;46(12):121–34. doi:10.1111/medu.12002
14. Ten Cate O. Entrustment decisions in clinical training. Med Educ. 2018;52(3):263–71.
doi:10.1111/medu.13436
15. Ellaway RH, et al. Situativity theory and medical education: theory and practice. Med Teach. 2018;40(2):123–9. doi:10.1080/0142159X.2017.1362701
16. Cruess RL, Cruess SR, Steinert Y. Supporting the development of professional identity: AMEE Guide No. 148. Med Teach. 2019;41(6):641–9. doi:10.1080/0142159X.2019.1617464
17. Jarvis-Selinger S, Pratt DD, Regehr G. Competency is not enough: integrating identity formation into medical education. Acad Med. 2012;87(9):1185–90. doi:10.1097/ACM.0b013e3182604968
18. Gruppen LD, et al. Conceptualizing medical teacher identity. Med Educ. 2019;53(9):865–77.
doi:10.1111/medu.13871
19. Stetson GV, et al. Professional identity formation and moral distress. Med Educ. 2020;54(2):123–34.doi:10.1111/medu.14012
20. Monrouxe LV. Identity, identification and medical education. Med Educ. 2010;44(1):40–9.doi:10.1111/j.1365-2923.2009.03499.
21. Blease C, et al. Artificial intelligence and the future of empathy in medicine. Lancet Digit Health. 2019;1(5):e231–e233. doi:10.1016/S2589-7500(19)30088-2
22. O’Connor M, et al. Emotional experiences in workplace learning. Med Educ. 2018;52(2):161–72.doi:10.1111/medu.13478
23. West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. Lancet. 2018;388(10057):2272–81. doi:10.1016/S0140-6736(16)31279-X
24. Schutz PA, Pekrun R. Emotion in education. Educ Psychol. 2017;52(4):233–41.
doi:10.1080/00461520.2017.1363403
25. Bland CJ, et al. Faculty development for educational innovation. Acad Med. 2018;93(1):62–70. doi:10.1097/ACM.0000000000001818
26. McLean M, Gibbs T. Twelve tips to design effective faculty development. Med Teach. 2019;41(1):1–6. doi:10.1080/0142159X.2018.1478846
27. Greenhalgh T, et al. Diffusion of innovations in health service organizations. Milbank Q. 2018;96(1):3–32.doi:10.1111/1468-0009.12334
28. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58. doi:10.1056/NEJMra1814259
29. Khanna N, et al. Teaching artificial intelligence ethics in medical education. Acad Med. 2022;97(3S):S95–S100.doi:10.1097/ACM.0000000000004523
30. London AJ. Artificial intelligence and black-box medical decisions. Hastings Cent Rep. 2019;49(1):15–21. doi:10.1002/hast.973

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