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Войти в исследования European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
Reference Hallucination in AI-Assisted Restorative Dentistry Education: An Educational Risk Assessment Using the Reference Hallucination Score Framework.
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Yildirim Manav Esra, Usta Sıla Nur
Аннотация
PURPOSE: Artificial intelligence (AI) chatbots are increasingly used by dental students and faculty for literature review, coursework preparation, and scholarly writing. In restorative dentistry education, where evidence-based decision-making is fundamental, inaccurate AI-generated citations may pose educational risks. This study evaluated the bibliographic reliability of AI-generated references in restorative dentistry topics and examined their potential implications for dental education.
METHODS: Five large language model (LLM)-based chatbots were tested using ten standardised restorative dentistry prompts representing commonly assigned educational topics. Each chatbot generated 100 references (N = 500). References were manually verified using indexed databases and evaluated using the Reference Hallucination Score (RHS; range 0-11). Chatbot performance was compared using the Friedman test, followed by Dunn-Bonferroni post hoc pairwise comparisons. Binary bibliographic error parameters were analysed using generalised linear mixed models, with chatbot type, prompt complexity, and their interaction as fixed effects and prompt as a random intercept (α = 0.05).
RESULTS: A significant overall difference in RHS was observed among the evaluated AI chatbots (p < 0.001), with post hoc analyses identifying significant differences between specific model pairs. ChatGPT-5.2 and Perplexity AI demonstrated the lowest hallucination severity, whereas Copilot and DeepSeek-v3.2 demonstrated the highest. Prompt complexity did not significantly influence hallucination severity. Errors most frequently involved author names, journal titles, and DOIs.
CONCLUSIONS: AI-generated references in restorative dentistry demonstrated substantial variability in bibliographic accuracy across the evaluated AI systems. These inaccuracies may pose potential risks to evidence-based learning and academic integrity, highlighting the importance of systematic reference verification and AI literacy in dental education.
METHODS: Five large language model (LLM)-based chatbots were tested using ten standardised restorative dentistry prompts representing commonly assigned educational topics. Each chatbot generated 100 references (N = 500). References were manually verified using indexed databases and evaluated using the Reference Hallucination Score (RHS; range 0-11). Chatbot performance was compared using the Friedman test, followed by Dunn-Bonferroni post hoc pairwise comparisons. Binary bibliographic error parameters were analysed using generalised linear mixed models, with chatbot type, prompt complexity, and their interaction as fixed effects and prompt as a random intercept (α = 0.05).
RESULTS: A significant overall difference in RHS was observed among the evaluated AI chatbots (p < 0.001), with post hoc analyses identifying significant differences between specific model pairs. ChatGPT-5.2 and Perplexity AI demonstrated the lowest hallucination severity, whereas Copilot and DeepSeek-v3.2 demonstrated the highest. Prompt complexity did not significantly influence hallucination severity. Errors most frequently involved author names, journal titles, and DOIs.
CONCLUSIONS: AI-generated references in restorative dentistry demonstrated substantial variability in bibliographic accuracy across the evaluated AI systems. These inaccuracies may pose potential risks to evidence-based learning and academic integrity, highlighting the importance of systematic reference verification and AI literacy in dental education.