{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:19:29Z","timestamp":1791227969691,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KAU Endowment"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dentistry Journal"],"abstract":"<jats:p>Objective: This study aimed to compare the accuracy of two large language models (LLMs)\u2014ChatGPT (version 3.5) and Google Gemini (formerly Bard)\u2014in answering dental caries-related multiple-choice questions (MCQs) using a simulated student examination framework across seven examination lengths. Materials and Methods: A total of 125 validated dental caries MCQs were extracted from Dental Decks and Oxford University Press question banks. Seven examination groups were constructed with varying question counts (25, 35, 45, 55, 65, 75, and 85 questions). For each group, 100 simulations were generated per LLM (ChatGPT and Gemini), resulting in 1400 simulated examinations. Each simulated student received a unique randomized subset of questions. MCQs were answered by each LLM using a standardized prompt to minimize ambiguity. Outcomes included mean score, passing rate (\u226560%), and performance differences between LLMs. Statistical analyses included independent t-tests, one-way ANOVA within each LLM, and two-way ANOVA examining interactions between LLM type and question count. Results: Across all seven examination formats, Gemini significantly outperformed ChatGPT (p &lt; 0.001). Gemini achieved higher passing rates and higher mean scores in every examination length. One-way ANOVA revealed significant score variation with increasing exam length for both LLMs (p &lt; 0.05). Two-way ANOVA demonstrated significant main effects of LLM type and question count, with no significant interaction. Randomization had no measurable effect on Gemini performance but influenced ChatGPT scores. Conclusions: Gemini demonstrated superior accuracy and higher passing rates compared to ChatGPT in all simulated examination formats. While both LLMs struggled with complex caries-related content, Gemini provided more reliable performance across question quantities. Educators should exercise caution in relying on LLMs for automated assessment or self-study, and future research should evaluate human\u2013AI hybrid models and LLM performance across broader dental domains.<\/jats:p>","DOI":"10.3390\/dj14020072","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T09:05:27Z","timestamp":1769504727000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessing the Efficacy of Artificial Intelligence Platforms in Answering Dental Caries Multiple-Choice Questions: A Comparative Study of ChatGPT and Google Gemini Language Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8749-4714","authenticated-orcid":false,"given":"Amr Ahmed","family":"Azhari","sequence":"first","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah P.O. Box 80200, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1810-8733","authenticated-orcid":false,"given":"Walaa Magdy","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah P.O. Box 80200, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Alhamadani","sequence":"additional","affiliation":[{"name":"Department of Data Science and Business Analytics, Florida Polytechnic University, Lakeland, FL 33801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amal","family":"Alfaraj","sequence":"additional","affiliation":[{"name":"Department of Prosthodontics, College of Dentistry, King Faisal University, Al Aahsa P.O. Box 80209, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8280-7886","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Northern Virginia Center, Falls Church, VA 22043, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Northern Virginia Center, Falls Church, VA 22043, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/09537325.2021.1971188","article-title":"Artificial intelligence (AI) healthcare technology innovations: The current state and challenges from a life science industry perspective","volume":"35","author":"Apell","year":"2023","journal-title":"Technol. Anal. Strat. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1016\/j.prosdent.2023.07.013","article-title":"Artificial intelligence in the detection and classification of dental caries","volume":"133","author":"Ahmed","year":"2023","journal-title":"J. Prosthet. Dent."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Al-Khalifa, K.S., Ahmed, W.M., Azhari, A.A., Qaw, M., Alsheikh, R., Alqudaihi, F., and Alfaraj, A. (2024). The Use of Artificial Intelligence in Caries Detection: A Review. Bioengineering, 11.","DOI":"10.3390\/bioengineering11090936"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20552076231216681","DOI":"10.1177\/20552076231216681","article-title":"Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition","volume":"9","author":"Azhari","year":"2023","journal-title":"Digit. Health"},{"key":"ref_5","first-page":"CD014545","article-title":"Imaging modalities to inform the detection and diagnosis of early caries","volume":"2021","author":"Walsh","year":"2021","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. Dent."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1136\/jramc-2018-001055","article-title":"Artificial intelligence and the radiologist: The future in the Armed Forces Medical Services","volume":"166","author":"Sen","year":"2020","journal-title":"BMJ Mil. Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.tips.2019.06.004","article-title":"Advancing Drug Discovery via Artificial Intelligence","volume":"40","author":"Chan","year":"2019","journal-title":"Trends. Pharmacol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e16934","DOI":"10.2196\/16934","article-title":"Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review","volume":"9","author":"Alturkistani","year":"2020","journal-title":"JMIR Res. Protoc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Boukenze, B., Mousannif, H., and Haqiq, A. (2016). Predictive Analytics in Healthcare System Using Data Mining Techniques. The Fourth International Conference on Database and Data Mining. Comput. Sci. Inf. Technol., 1\u20139.","DOI":"10.5121\/csit.2016.60501"},{"key":"ref_11","unstructured":"Oseni, A., Moustafa, N., Janicke, H., Liu, P., Tari, Z., and Vasilakos, A.V. (2021). Security and Privacy for Artificial Intelligence: Opportunities and Challenges. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Roselli, D., Matthews, J., and Talagala, N. (2019). Managing Bias in AI. Proceedings of the Companion Proceedings of The 2019 World Wide Web Conference, Association for Computing Machinery.","DOI":"10.1145\/3308560.3317590"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Alam, A. (2021, January 3\u20134). Should Robots Replace Teachers? Mobilisation of AI and Learning Analytics in Education. Proceedings of the 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), Mumbai, India.","DOI":"10.1109\/ICAC353642.2021.9697300"},{"key":"ref_14","unstructured":"Hao, K. (2019). China has started a grand experiment in AI education. It could reshape how the world learns. MIT Technol. Rev., 123."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.procs.2018.08.233","article-title":"Artificial Intelligence trends in education: A narrative overview","volume":"1","author":"Chassignol","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","unstructured":"Pedro, F., Subosa, M., Rivas, A., and Valverde, P. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development, United Nations Educational, Scientific and Cultural Organization."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e28198","DOI":"10.1016\/j.heliyon.2024.e28198","article-title":"The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models","volume":"10","author":"Ahmed","year":"2024","journal-title":"Heliyon"},{"key":"ref_18","unstructured":"Atlas, S. (2023). ChatGPT for Higher Education and Professional Development: A Guide to Conversational AI, University of Rhode Island Libraries."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kastrati, Z., Dalipi, F., Imran, A.S., Pireva Nuci, K., and Wani, M.A. (2021). Sentiment Analysis of Students\u2019 Feedback with NLP and Deep Learning: A Systematic Mapping Study. Appl. Sci., 11.","DOI":"10.3390\/app11093986"},{"key":"ref_20","unstructured":"Vincent-Lancrin, S.a.R.v.d.V. (2020). Trustworthy Artificial Intelligence (AI) in Education: Promises and Challenges, OECD Publishing. OECD Education Working Papers."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1111\/jerd.13046","article-title":"Implications of large language models such as ChatGPT for dental medicine","volume":"35","author":"Eggmann","year":"2023","journal-title":"J. Esthet. Restor. Dent."},{"key":"ref_22","unstructured":"Davenport, T.H., Barth, P., and Bean, R. (2012). How big data is different. MIT Sloan Manag. Rev., 54."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Deoras, A., Povey, D., Burget, L., and \u010cernock\u00fd, J. (2011). Strategies for training large scale neural network language models. Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, IEEE.","DOI":"10.1109\/ASRU.2011.6163930"},{"key":"ref_24","unstructured":"Silberg, J., and Manyika, J. (2019). Notes from the AI Frontier: Tackling Bias in AI (and in Humans), McKinsey Global Institute."},{"key":"ref_25","unstructured":"Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., and Gehrmann, S. (2022). Palm: Scaling language modeling with pathways. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hasib, K.M., Rahman, F., Hasnat, R., and Alam, M.G.R. (2022). A Machine Learning and Explainable AI Approach for Predicting Secondary School Student Performance. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), IEEE.","DOI":"10.1109\/CCWC54503.2022.9720806"},{"key":"ref_27","unstructured":"Libbrecht, P., Declerck, T., Schlippe, T., Mandl, T., and Schiffner, D. (2020, January 19\u201320). NLP for student and teacher: Concept for an AI based information literacy tutoring system. Proceedings of the CIKM (Workshops), Galway, Ireland."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ahmad, S.F., Rahmat, M.K., Mubarik, M.S., Alam, M.M., and Hyder, S.I. (2021). Artificial Intelligence and Its Role in Education. Sustainability, 13.","DOI":"10.3390\/su132212902"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1177\/0022034520969115","article-title":"Application of artificial intelligence in dentistry","volume":"100","author":"Shan","year":"2021","journal-title":"J. Dent. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Suarez, A., Adanero, A., Diaz-Flores Garcia, V., Freire, Y., and Algar, J. (2022). Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students\u2019 Diagnostic Skills. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19148735"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepano, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., and Maningo, J. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digit. Health, 2.","DOI":"10.1371\/journal.pdig.0000198"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S., Yin, H., Xu, C., Yang, R., and Zheng, Q. (2023). ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model. arXiv.","DOI":"10.1038\/s41368-023-00239-y"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e223312","DOI":"10.1148\/radiol.223312","article-title":"ChatGPT and the Future of Medical Writing","volume":"307","author":"Biswas","year":"2023","journal-title":"Radiology"},{"key":"ref_34","first-page":"1","article-title":"Voice Assistant Integrated with Chat GPT","volume":"28","author":"Shafeeg","year":"2023","journal-title":"Indones. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0140-6736(07)60031-2","article-title":"Dental caries","volume":"369","author":"Selwitz","year":"2007","journal-title":"Lancet"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s11282-021-00577-9","article-title":"Deep-learning approach for caries detection and segmentation on dental bitewing radiographs","volume":"38","author":"Bayrakdar","year":"2022","journal-title":"Oral Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting caries lesions of different radiographic extension on bitewings using deep learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J. Dent."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1177\/0022034519871884","article-title":"Caries Detection with Near-Infrared Transillumination Using Deep Learning","volume":"98","author":"Casalegno","year":"2019","journal-title":"J. Dent. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.tripleo.2008.03.002","article-title":"An artificial multilayer perceptron neural network for diagnosis of proximal dental caries","volume":"106","author":"Devito","year":"2008","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gomez, J. (2015). Detection and diagnosis of the early caries lesion. BMC Oral Health, 15.","DOI":"10.1186\/1472-6831-15-S1-S3"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.prosdent.2013.07.014","article-title":"Dental caries: An updated medical model of risk assessment","volume":"111","author":"Kutsch","year":"2014","journal-title":"J. Prosthet. Dent."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103260","DOI":"10.1016\/j.jdent.2019.103260","article-title":"Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study","volume":"92","author":"Schwendicke","year":"2020","journal-title":"J. Dent."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.36740\/WLek202007135","article-title":"Application of neural network technologies in the dental caries forecast","volume":"73","author":"Udod","year":"2020","journal-title":"Wiadomo\u015bci Lek."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e16242","DOI":"10.5812\/iranjradiol.12(2)2015.16242","article-title":"Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth","volume":"12","author":"Valizadeh","year":"2015","journal-title":"Iran. J. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e48291","DOI":"10.2196\/48291","article-title":"Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions","volume":"9","author":"AlSaad","year":"2023","journal-title":"JMIR Med. Educ."},{"key":"ref_46","first-page":"e38317","article-title":"ChatGPT in Dentistry: A Comprehensive Review","volume":"15","author":"Alhaidry","year":"2023","journal-title":"Cureus"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Puleio, F., Lo Giudice, G., Bellocchio, A.M., Boschetti, C.E., and Lo Giudice, R. (2024). Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review. Appl. Sci., 14.","DOI":"10.3390\/app142310802"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Murdoch, B. (2021). Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Med. Ethics., 22.","DOI":"10.1186\/s12910-021-00687-3"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Silvestri, S., Islam, S., Papastergiou, S., Tzagkarakis, C., and Ciampi, M. (2023). A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem. Sensors, 23.","DOI":"10.3390\/s23020651"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00146-017-0693-8","article-title":"AI and education: The importance of teacher and student relations","volume":"34","author":"Guilherme","year":"2019","journal-title":"AI Soc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1007\/s40593-021-00285-9","article-title":"Algorithmic Bias in Education","volume":"32","author":"Baker","year":"2022","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"ref_52","unstructured":"Boiko, D.A., MacKnight, R., and Gomes, G. (2023). Emergent autonomous scientific research capabilities of large language models. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hind, M., Houde, S., Martino, J., Mojsilovic, A., Piorkowski, D., Richards, J., and Varshney, K.R. (2020, January 25\u201330). Experiences with improving the transparency of AI models and services. Proceedings of the InExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3334480.3383051"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S., O\u2019Brien, D., Scott, K., Schieber, S., Waldo, J., and Weinberger, D. (2017). Accountability of AI under the law: The role of explanation. arXiv.","DOI":"10.2139\/ssrn.3064761"},{"key":"ref_55","first-page":"100363","article-title":"Fairness and accountability of AI in disaster risk management: Opportunities and challenges","volume":"2","author":"Gevaert","year":"2021","journal-title":"Pattern Recognit."}],"container-title":["Dentistry Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-6767\/14\/2\/72\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T09:11:15Z","timestamp":1769591475000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-6767\/14\/2\/72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["dj14020072"],"URL":"https:\/\/doi.org\/10.3390\/dj14020072","relation":{},"ISSN":["2304-6767"],"issn-type":[{"value":"2304-6767","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}