Future of Medical Education Journal

Future of Medical Education Journal

Impact of Artificial Intelligence and Generative AI on Dental Education

Document Type : Narrative Review

Authors
1 Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran
2 Department of Pediatric Dentistry, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract
Background: Artificial intelligence (AI), particularly generative AI (GenAI), is fundamentally transforming dental education by enabling highly personalized learning and refining clinical decision-making. However, the rapid adoption of tools like ChatGPT has outpaced the establishment of formal educational guidelines, thereby introducing novel ethical and practical concerns.
Methods: A comprehensive narrative review was conducted, synthesizing current literature and authoritative reviews published since 2020, with a focus on the integration of GenAI integration in dental education. Additional sources were identified from key bibliographies. This review critically evaluates the impacts, challenges, and optimal strategies for incorporating AI across undergraduate and postgraduate dental curricula.
Results: AI tools, especially GenAI, are increasingly being integrated into teaching, learning, and assessment. Documented benefits include personalized feedback, realistic clinical case simulations, and improved diagnostic reasoning. These technologies promote educational efficiency, pedagogical innovation, and student engagement. However, significant risks remain, including threats to academic integrity (e.g., AI-generated assignments), algorithmic bias, and data privacy issues. Most dental institutions currently lack discipline-specific AI policies, though broader university and global guidelines emphasize ethical use, transparency, and critical thinking. Successful integration depends on comprehensive faculty development, clear ethical frameworks, and student literacy regarding the capabilities and limitations of AI.
Conclusion: The integration of AI and GenAI into dental education not only offers transformative potential but also introduces unique challenges. Proactive curriculum development and institutional policies are essential to harness these technologies effectively while upholding academic integrity and ethical standards.
Keywords
Subjects

1.         Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023;13(2):150.
2.         Mirzaei A, Mollaei M, Lotfizadeh A, Aryana M, Ebrahimpour A. Application and challenges of artificial intelligence in different branches of dentistry. J Craniomaxillofac Res. 2025;11(4):215-22. doi:10.18502/jcr.v11i4.18709.
3.         Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198.
4.         Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, et al. GPT-4 technical report. arXiv. 2023;arXiv: 2303.08774.
5.         Uribe SE, Maldupa I, Schwendicke F. Integrating generative AI in dental education: a scoping review of current practices and recommendations. Eur J Dent Educ. 2025;29(2):341-55.
6.         Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930.
7.         Iyer P, Aziz K, Ojcius DM. Impact of COVID-19 on dental education in the United States. J Dent Educ. 2020;84(6):718-22.
8.         Cheng F-C, He Y-Z, Wang L-H, Chang JY-F, Liu S-Y, Chang Y-T, et al. Comparison of past and current dental school curricula for dental students of National Taiwan University. J Dent Sci. 2022;17(3):1169-79.
9.         Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769-74.
10.       Khanagar SB, Naik S, Al Kheraif AA, Vishwanathaiah S, Maganur PC, Alhazmi Y, et al. Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review. Diagnostics. 2021;11(6):1004.
11.       Maity S, Deroy A. Generative AI and its impact on personalized intelligent tutoring systems. arXiv. 2024;arXiv:241010650.
12.       Chan J, Li Y. Enhancing higher education with generative AI: a multimodal approach for personalised learning. arXiv. 2025;arXiv:250207401.
13.       Mittal U, Sai S, Chamola V, Sangwan D. A comprehensive review on generative AI for education. IEEE Access. 2024;12:142733-59.
14.       Alwadei AH, Tekian AS, Brown BP, Alwadei FH, Park YS, Alwadei SH, et al. Effectiveness of an adaptive eLearning intervention on dental students’ learning in comparison to traditional instruction. J Dent Educ. 2020;84(11):1294-302.
15.       Kolts RJ, Balaban CM, Zasso C, Reich R, Ryff B, Raina A. The impact of dental artificial intelligence for radiograph analysis. Compend Contin Educ Dent. 2023;44(1):e1-e4.
16.       Dhagare MRP. Generative AI and education: a symbiotic relationship. Int J Res Appl Sci Eng Technol. 2024.
17.       Alali R, Wardat Y, Al-Saud K, Alhayek KA. Generative AI in education: best practices for successful implementation. Int J Relig. 2024
18.       Deng X, Joshi KD. Promoting ethical use of generative AI in education. ACM SIGMIS Database. 2024;55:6-11.
19.       Jawalkar AA, Gothane S, Bruno A. Generative AI: a structured review, techniques, application and future prospects. Int J Res Advent Technol (IJRAT). 2024;12(4):14-20. doi:10.32622/ijrat.124202408.
20.       Stogiannos N, O'Regan T, Scurr E, Litosseliti L, Pogose M, Harvey H, et al. AI implementation in the UK landscape: knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. Radiography. 2024;30(2):612-21.
21.       Kim CS, Samaniego CS, Sousa Melo SL, Brachvogel WA, Baskaran K, Rulli D. Artificial intelligence (AI) in dental curricula: ethics and responsible integration. J Dent Educ. 2023;87(11):1570-3.
22.       Busch F, Hoffmann L, Truhn D, Ortiz-Prado E, Makowski MR, Bressem KK, et al. Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties. BMC Med Educ. 2024;24(1):1066.
23.       Lombardi D, Traetta L, Maffei A, Podžaj P. Evolving educational horizons: integrating AI with innovative teaching and assessment strategies. Educ Sci Soc. 2025;15(2):185-203. doi:10.3280/ess2-2024oa18462.
24.       Harte M, Carey B, Feng QJ, Alqarni AA, Albuquerque R. Transforming undergraduate dental education: the impact of artificial intelligence. Br Dent J. 2025;238:57-60.
25.       Ng DTK, Wu W, Leung JKL, Chiu TKF, Chu SK-W. Design and validation of the AI literacy questionnaire: the affective, behavioural, cognitive and ethical approach. Br J Educ Technol. 2024;55(3):1082-104. doi:10.1111/bjet.13411.
26.       Tenberga I, Daniela L. Artificial intelligence literacy competencies for teachers through self-assessment tools. Sustainability. 2024.
27.       Al-Zubaidi SM, Muhammad Shaikh G, Malik A, Zain Ul Abideen M, Tareen J, Alzahrani NSA, et al. Exploring faculty preparedness for artificial intelligence-driven dental education: a multicentre study. Cureus. 2024;16
28.       Rasul T, Nair S, Kalendra D, Balaji MS, Santini FdO, Ladeira WJ, et al. Enhancing academic integrity among students in GenAI era: a holistic framework. Int J Manag Educ. 2024
29.       Zlotnikova I, Hlomani H, Mokgetse T, Bagai K. Establishing ethical standards for GenAI in university education: a roadmap for academic integrity and fairness. J Inf Commun Ethics Soc. 2025;23:188-216.
30.       Lin GSS, Tan WW, Hashim H. Students' perceptions towards the ethical considerations of using artificial intelligence algorithms in clinical decision-making. Br Dent J. 2024
31.       Hanna MG, Pantanowitz L, Jackson B, Palmer O, Visweswaran S, Pantanowitz J, et al. Ethical and Bias Considerations in Artificial Intelligence/Machine Learning. Mod Pathol. 2025;38(3):100686. doi:10.1016/j.modpat.2024.100686. Epub 2024-12-16.
32.       Charles Nagy S, McInnes R, Airey L. Gen-AI: Gen-AI: a transformative partner in collaborative course development. Int J Innov Online Educ. 2023
33.       Shiohira K, Holmes W. Proceed with caution: the pitfalls and potential of AI and education. In: Araya D, Marber P, editors. Augmented Education in the Global Age. New York: Routledge; 2023. p. 138–56. doi:10.4324/9781003230762-11.
34.       Cole JR, Dodge WW, Findley JS, Horn BD, Kalkwarf KL, Martin MM, et al. Interprofessional collaborative practice: how could dentistry participate? J Dent Educ. 2018;82(5):441-5.
35.       Stafford GL. Fostering dental faculty collaboration with an evidence-based decision making model designed for curricular change. J Dent Educ. 2014;78(3):349-58.
36.       Qutieshat A. Dental education at a crossroad: questioning institutionalized education and embracing the liberating potentials of artificial intelligence. Jordan J Dent. 2025.