Implementing Artificial Intelligence in Dental Education: An International Survey of Experiences and Opinions of Dental Students
DOI:
https://doi.org/10.58600/eurjther2846Keywords:
dental education, medical education, maxillofacial radiology, oral healthcareAbstract
Objective: Innovative educational tools support student-centered learning by enhancing the explanation, interpretation, and visualization of radiologic findings. The study aimed to evaluate the utilization of artificial intelligence (AI)-based educational decision-support system by participants, as well as their perspectives and expectations regarding AI and its implementation in the curriculum.
Methods: Undergraduate dental students of two nationalities participated in a cross-sectional online survey. The assessment of their perceptions and attitudes toward AI in dental education was done by a 17-question questionnaire. Descriptive statistics were presented utilizing the median, min-max, mean, standard deviation, and interquartile range. The Mann-Whitney U test was applied to compare the responses of individuals. The Chi-Square test was conducted to investigate the association of gender and nationality concerning the use of AI.
Results: The study included 102 dental students (62 males, 40 females) from two different nationalities (62 Turkish, 40 American). The findings showed that the majority of dental students thought using AI in dental education was beneficial. AI usage distribution showed no significant difference by nationality or gender, but concerns about AI replacing dentists and its role in dental education differed significantly between nationalities.
Conclusion: Students largely offered a favorable attitude toward AI. These results can assist lecturers in formulating effective approaches to optimize the benefits of AI in dental education, address any issues, and integrate AI into the dental curriculum.
References
[1] Hinton G (2018) Deep learning: a technology with the potential to transform health care. JAMA. 320(11):1101. https://doi.org/10.1001/jama.2018.11100
[2] Yüzbaşıoğlu E (2021) Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 85(1):60-68. https://doi.org/10.1002/jdd.12385
[3] Escobar-Castillejos D, Noguez J, Neri L, Magana A, Benes B (2016) A review of simulators with haptic devices for medical training. J Med Syst. 40(4):104. https://doi.org/10.1007/s10916-016-0459-8
[4] Tadinada A, Gul G, Godwin L, Al Sakka Y, Crain G, Stanford CM, Johnson J (2023) Utilizing an organizational development framework as a road map for creating a technology-driven agile curriculum in predoctoral dental education. J Dent Educ. 87(3):394-400. https://doi.org/10.1002/jdd.13131
[5] Bashook P, Parboosingh J (1998) Contiuning Medical Education: Recertification and the maintenance of competence. BMJ. 316(7130):545-548. https://doi.org/10.1136/bmj.316.7130.545
[6] Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, Hawkins CM (2018) Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol. 15(2):350-359. https://doi.org/10.1016/j.jacr.2017.09.044
[7] Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S (2019) Artificial intelligence for precision education in radiology. Br J Radiol. 92(1103):20190389. https://doi.org/10.1259/bjr.20190389
[8] Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar K (2023) Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 13(2):150. https://doi.org/10.3390/educsci13020150
[9] Schwendicke F, Samek W, Krois J (2020) Artificial intelligence in dentistry: chances and challenges. J Dent Res. 99(7):769-774. https://doi.org/10.1177/0022034520915714
[10] Johnston SC (2018) Anticipating and training the physician of the future: the importance of caring in an age of artificial intelligence. Acad Med. 93(8):1105-1106. https://doi.org/10.1097/ACM.0000000000002175
[11] Roll I, Wylie R (2016) Evolution and revolution in artificial intelligence in education. Int J Artif Intell Educ. 26:582-599. https://doi.org/10.1007/s40593-016-0110-3
[12] Bayne S (2015) Teacherbot: interventions in automated teaching. Teach High Educ. 20(4):455-467. https://doi.org/10.1080/13562517.2015.1020783
[13] Botrel L, Holz E, Kübler A (2015) Brain Painting V2: evaluation of P300-based brain-computer interface for creative expression by an end-user following the user-centered design. Brain-Comput Interfaces. 2(2-3):135-149. https://doi.org/10.1080/2326263X.2015.1100038
[14] Chan KS, Zary N (2019) Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 5(1):e13930. https://doi.org/10.2196/13930
[15] Keser G, Namdar Pekiner FM (2021) Attitudes, perceptions, and knowledge regarding the future of artificial intelligence in oral radiology among a group of dental students in Turkey: a survey. Clin Exp Health Sci. 11(4):637-641. https://doi.org/10.33808/clinexphealthsci.928246
[16] Uribe SE, Maldupa I, Schwendicke F (2025) Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations. Eur J Dent Educ. 29(2):341-355. https://doi.org/10.1111/eje.13074
[17] El-Hakim M, Anthonappa R, Fawzy A (2025) Artificial Intelligence in Dental Education: A Scoping Review of Applications, Challenges, and Gaps. Dent J (Basel). 13(9):384. https://doi.org/10.3390/dj13090384
[18] Claman D, Sezgin E (2024) Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models. JMIR Med Educ. 10(1):e52346. https://doi.org/10.2196/52346
[19] Kong M, Fok EHW, Yiu CKY (2025) A Scoping Review of Large Language Models in Dental Education: Applications, Challenges, and Prospects. Int Dent J. 75(6):103854. https://doi.org/10.1016/j.identj.2025.103854
[20] Wei H, Dai Y, Yuan K, Li KY, Hung KF, Hu EM, Li X (2025) AI-Powered Problem- and Case-based Learning in Medical and Dental Education: A Systematic Review and Meta-analysis. Int Dent J. 75(6):100858. https://doi.org/10.1016/j.identj.2025.100858
[21] Koçak B, Ponsiglione A, Stanzione A, Bluethgen C, Santinha J, Ugga L, Cuocolo R (2025) Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects. Diagn Interv Radiol. 31(2):75-88. https://doi.org/10.4274/dir.2024.242854
[22] Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR (2021) Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod. 22(1):18. https://doi.org/10.1186/s40510-021-00361-9
[23] Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K (2022) Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 51(1):20210197. https://doi.org/10.1259/dmfr.20210197
[24] Carrillo-Perez F, Pecho OE, Morales JC, Paravina RD, Della Bona A, Ghinea R, Herrera LJ (2022) Applications of artificial intelligence in dentistry: a comprehensive review. J Esthet Restor Dent. 34(1):259-280. https://doi.org/10.1111/jerd.12844
[25] Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC (2020) Dental caries diagnosis and detection using neural networks: a systematic review. J Clin Med. 9(11):3579. https://doi.org/10.3390/jcm9113579
[26] Coşkun S, Güngör MA (2023) Comparative study of use of artificial intelligence in oral radiology education. Eur Arch Dent Sci. 50(1):41-46. https://doi.org/10.52037/eads.2023.0009
[27] Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Dreyer KJ (2018) Current applications and future impact of machine learning in radiology. Radiology. 288(2):318-328. https://doi.org/10.1148/radiol.2018171820
[28] Kotsiantis SB (2012) Use of machine learning techniques for educational purposes: a decision support system for forecasting students’ grades. Artif Intell Rev. 37(4):331-344. https://doi.org/10.1007/s10462-011-9234-x
[29] Reeder MM, Felson B (2003) Gamuts in radiology, 4th edn. Springer-Verlag, New York.
[30] Islam NM, Laughter L, Sadid‐Zadeh R, Smith C, Dolan TA, Crain G, Squarize CH (2022) Adopting artificial intelligence in dental education: a model for academic leadership and innovation. J Dent Educ. 86(11):1545-1551. https://doi.org/10.1002/jdd.13010
[31] Yılmaz C, Erdem RZ, Altınok Uygun L (2024) Artificial intelligence knowledge, attitudes and application perspectives of undergraduate and specialty students of faculty of dentistry in Turkey: an online survey research. BMC Med Ed. 24(1):1149. https://doi.org/10.1186/s12909-024-06106-6
[32] Hegde S, Nanayakkara S, Jordan A, Jeha O, Patel U, Luu V, Gao J (2025) Attitudes and perceptions of Australian dentists and dental students towards applications of artificial intelligence in dentistry: a survey. Eur J Dent Educ. 29(1):9-18. https://doi.org/10.1111/eje.13042
[33] Veerabhadrappa SK, Hong TTC, Padarha S, Bharadwaj A, Qian TJ, King TK, Yadav S (2025). Exploring Knowledge, Attitude, and Perceptions toward the Artificial Intelligence among Malaysian Clinical-Year Dental Undergraduate Students: A Cross-sectional Survey. J. Datta Meghe Inst. Med. Sci. Univ. 20(3):622-629. https://doi.org/10.4103/jdmimsu.jdmimsu_208_25
[34] Kumari M (2023) Perception of dental students in incorporating artificial intelligence into dental education. J Adv Sci. 2(1):41-45. https://doi.org/10.58935/joas.v2i1.27
[35] Martin D (2020) A guide to critical thinking: implications for dental education. Br Dent J. 229(1):52-53. https://doi.org/10.1038/s41415-020-1648-x
[36] Dewey J (2008) Handbook of research on teacher education, 3rd edn. Routledge, New York.
[37] Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, Tryfonos O, Krois J, IADR e-oral health network and the ITU/WHO focus group AI for health (2023) Artificial intelligence for oral and dental healthcare: core education curriculum. J Dent. 128:104363. https://doi.org/10.1016/j.jdent.2022.104363
[38] Sharab L, Butul B, Guha U (2024) Integrating critical thinking and embracing artificial intelligence: dual pillars for advancing dental education. Saudi Dent J. 36(12):1660-1667. https://doi.org/10.1016/j.sdentj.2024.11.004
[39] Ghasemian A, Salehi M, Ghavami V, Yari M, Tabatabaee SS, Moghri J (2025) Exploring dental students' attitudes and perceptions toward artificial intelligence in dentistry in Iran. BMC Med Ed. 25(1):725. https://doi.org/10.1186/s12909-025-07220-9
[40] Elchaghaby M, Wahby R (2025) Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: a cross-sectional study. BMC Oral Health. 25(1):11. https://doi.org/10.1186/s12903-024-05282-7
[41] Karan-Romero M, Salazar-Gamarra RE, Leon-Rios XA (2023) Evaluation of Attitudes and Perceptions in Students about the Use of Artificial Intelligence in Dentistry. Dent J. 11(5):125. https://doi.org/10.3390/dj11050125
[42] Jeong H, Han SS, Jung HI, Lee W, Jeon KJ (2024) Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority. BMC Med Educ. 24(1):430. https://doi.org/10.1186/s12909-024-05441-y
[43] Halat DH, Shami R, Daud A, Sami W, Soltani A, Malki A (2024) Artificial intelligence readiness, perceptions, and educational needs among dental students: a cross-sectional study. Clin Exp Dent Res. 10(4):e925. https://doi.org/10.1002/cre2.925
[44] Aboalshamat KT (2022) Perception and utilization of artificial intelligence (AI) among dental professionals in Saudi Arabia. Open Dent J. 16(1). https://doi.org/10.2174/18742106-v16-e2208110
[45] Roganović J, Radenković M, Miličić B (2023) Responsible use of artificial intelligence in dentistry: survey on dentists’ and final-year undergraduates’ perspectives. Healthcare (Basel). 11(10):1480. https://doi.org/10.3390/healthcare11101480
[46] Shimizu I, Kasai H, Shikino K, Araki N, Takahashi Z, Onodera M, Kawakami E (2023) Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study. JMIR Med Educ. 9(1):e53466. https://doi.org/10.2196/53466
[47] Jalali A, Harbi Houssein K, Fotsing S (2025) Twelve Practical Tips for Integrating AI Into Medical Education: Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains. JMIR Med Educ. 11:e81297. https://doi.org/10.2196/81297
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Sinem Coşkun Albayrak, Sevda Kurt Bayrakdar, İbrahim Şevki Bayrakdar, Rohan Jagtab

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The content of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.









