Faculty Insights on Artificial Intelligence Chatbot Integration in Dental Implant Clinical Education

In the predoctoral dental clinical education at the University of Illinois Chicago, College of Dentistry (UIC-COD), faculty encounter substantial workloads addressing frequent student inquiries pertaining to implant-related workflow protocols and procedures. Timely access to accurate clinical information is crucial, yet traditional resources often lack immediacy and interactivity. Given the increasing popularity of artificial intelligence (AI) chatbots in education, exploring faculty perceptions of integrating such technology into predoctoral dental implant clinical education was essential to address potential benefits and identify concerns prior to broad implementation. The authors piloted an AI Chatbot (Figure 1) specifically developed from a curated database of 1,300 implant-related question-answer pairs previously validated by predoctoral implant faculty at UIC-COD. Twelve predoctoral faculty members from restorative and periodontics departments voluntarily consented to evaluate the Chatbot through clinical inquiry scenarios. Faculty interacted with the Chatbot and subsequently provided qualitative (advantages, disadvantages, and areas of improvement) via interviews and quantitative (structured Likert scale from strongly disagree = 1 to strongly agree = 5) feedback via a survey assessing AI awareness, user engagement, educational value, technological issues, and privacy and accuracy concerns. Participants reported strong adaptability to new technologies (mean ± SD; 4.1 ± 1.2) and recognized AI's potential to enhance educational experiences (4.5 ± 0.7). Faculty positively rated the Chatbot's timely responses (4.5 ± 0.5), its capability to reduce their clinical workload (4.4 ± 1.3), and its effectiveness in making learning more efficient (4.5 ± 0.9). The Chatbot was viewed as interactive (4.2 ± 1.1), user-friendly (4.2 ± 0.7), and sufficiently valuable for faculty to recommend its use to peers (4.5 ± 2.0). Participants particularly emphasized the Chatbot's role in providing prompt, engaging, and anxiety-reducing educational support. However, faculty concerns surfaced regarding Chatbot integration. Accuracy of provided information (3.9 ± 0.6), potential misleading responses (2.8 ± 1.3), privacy concerns (2.9 ± 1.0), questionable reliability of data sources (2.5 ± 0.8), and reduction of essential human interaction (1.9 ± 0.8) were identified as areas requiring careful attention (Table 1). Qualitative analysis (Table 2) indicated additional caution related to over-reliance on Chatbot technology, the necessity for frequent updates, technical limitations, and the lengthiness of some responses. Faculty-recommended areas for improvement include incorporating student feedback, integrating chatbots more seamlessly into the curriculum, providing multi-language support, and enhancing data protection measures. Faculty perceptions highlighted significant promise for the integration of AI chatbots into predoctoral dental implant clinical education. Faculty experienced a decrease in routine workload, students received timely and personalized assistance, and overall efficiency of clinical education efficiency improved. Nonetheless, ensuring content accuracy, preserving human interaction, and addressing privacy concerns remain critical priorities. Effective implementation of this innovation will require continuous content verification, rigorous training, maintenance of human oversight, and careful integration into existing educational frameworks. The pilot study has shown Chatbot technology's potential to be a valuable adjunctive resource rather than a standalone replacement, indicating that careful, thoughtful adoption can advance dental education through innovation. The authors declare no conflicts of interest.