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Generative AI-Based Immersive Training for Endodontic Risk Disclosure and Informed Consent Among Dental Students
Sponsor: The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine
Summary
The objective of this prospective, parallel-group, 1:1 randomized controlled trial is to evaluate the effectiveness of a generative artificial intelligence (AI)-based immersive training program, utilizing the Gemini platform, in improving endodontic risk disclosure and informed consent (RD-IC) quality, communication competence, self-efficacy, and reducing communication anxiety among undergraduate dental students during their clinical clerkship. Traditional pedagogical methods in dental education, such as case-based learning (CBL) and peer role-playing, often struggle to simulate the high-stakes interpersonal tension and technical complexity involved in specialized endodontic risk disclosure. Complications like instrument separation, pulp floor perforation, and acute flare-ups require clear, empathetic, and legally sound communication. While standardized patients (SPs) are effective, they pose severe logistical and financial constraints for large student cohorts. To address this gap, 96 fifth-year undergraduate dental students undergoing clinical rotation were recruited and randomly allocated to either the experimental group (1-month generative AI-based immersive training via the Gemini platform) or the control group (1-month conventional case-based learning). The AI intervention provided interactive, real-time conversational simulations across six high-stakes endodontic clinical scenarios, delivering instantaneous, personalized feedback on technical accuracy and empathetic delivery. The primary outcome evaluated was the RD-IC completeness score. Secondary outcomes included SEGUE communication framework scores, Self-Efficacy in Clinical Communication Scale (SCS) scores, Communication Anxiety Scale (CAS) scores, blinded standardized patient (SP) ratings, user satisfaction, and long-term clinical skill retention assessed at 1-month (T2-1m) and 2-month (T2-2m) post-intervention follow-ups.
Official title: Generative AI-Based Immersive Training for Endodontic Risk Disclosure and Informed Consent Among Dental Students: A Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - 25 Years
Study Type
INTERVENTIONAL
Enrollment
96
Start Date
2026-03-01
Completion Date
2026-05-31
Last Updated
2026-10-15
Healthy Volunteers
Yes
Interventions
Generative AI-Based Immersive Training
1-month generative AI-based immersive training program utilizing the Gemini platform. Participants engage in 10-15 minute adaptive conversational simulations every 3-4 days covering six high-stakes endodontic clinical scenarios (such as acute pain management, instrument separation disclosure, and pulp floor perforation), receiving immediate personalized feedback on risk disclosure completeness and empathetic communication.
Conventional Case-Based Learning
1-month conventional case-based learning curriculum targeting the exact same six high-stakes endodontic clinical scenarios. Learning involves structured case discussions, faculty-led lectures, and review of standardized informed consent templates without adaptive conversational immersion or real-time AI feedback.
Locations (1)
The First Affiliated Hospital of Hunan University of Chinese Medicine
Changsha, Hunan, China