Generative AI-Based Immersive Training for Endodontic Risk Disclosure and Informed Consent Among Dental Students

NCT07731867 | Completed

• 3 other identifiers: MR-43-26-0465122023-JG0482026-JG060
• Study Type: interventional
• Target: 96
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Endodontic Diseases; Clinical Communication; Informed Consent

Keywords

Generative artificial intelligence; Immersive simulation; Endodontics; Risk disclosure; Informed consent

Outcome Measures

Primary Outcomes (1)

• Endodontic Risk Disclosure and Informed Consent (RD-IC) Completeness Score

  Evaluated via a validated 10-item checklist covering core indications, intraoperative complications, postoperative pain, long-term structural defects, poor prognosis, anatomical variations, sequential treatments, anesthetic allergies, systemic diseases, and poor compliance. Scores range from 0 to 100, with higher scores indicating superior technical accuracy and completeness in risk disclosure.

  Immediately post-intervention (T1, at 1 month after study baseline)

Secondary Outcomes (7)

• SEGUE Framework Doctor-Patient Communication Competence Score

  Immediately post-intervention (T1, at 1 month after study baseline)

• Clinical Communication Self-Efficacy Scale (SCS) Score

  Immediately post-intervention (T1, at 1 month after study baseline)

• Communication Anxiety Scale (CAS) Score

  Immediately post-intervention (T1, at 1 month after study baseline)

• Blinded Standardized Patient (SP) Core Clinical Communication Competence Score

  Immediately post-intervention (T1, at 1 month after study baseline)

• AI Simulation Teaching Experience Scale Score

  Immediately post-intervention (T1, at 1 month after study baseline)

Study Arms (2)

Experimental Group

EXPERIMENTAL

Participants received a 1-month generative AI-based immersive training program using the Gemini platform, engaging in standardized interactive simulations of six high-stakes endodontic clinical scenarios with immediate personalized feedback.

Other: Generative AI-Based Immersive Training

Control Group

ACTIVE COMPARATOR

Participants underwent conventional case-based learning (CBL) targeting the same six endodontic scenarios over 1 month, involving group case discussions, faculty lectures, and standard informed consent reviews.

Other: Conventional Case-Based Learning

Interventions

Generative AI-Based Immersive Training OTHER

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.

Experimental Group

Conventional Case-Based Learning OTHER

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.

Control Group

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Fifth-year undergraduate dental students undergoing clinical clerkship at the Department of Stomatology, Hunan University of Chinese Medicine.
• Completed all core didactic and theoretical courses in endodontics.
• Entered the early stage of clinical rotation.
• Provided signed voluntary informed consent to participate in the trial.

You may not qualify if:

• Prior experience with specialized clinical communication simulation or specialized AI communication training.
• Presence of unavoidable schedule conflicts during the study period.
• Factors affecting student eligibility to complete the full training and evaluation process.

Sponsors & Collaborators

• The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine: lead

Study Sites (1)

The First Affiliated Hospital of Hunan University of Chinese Medicine

Changsha, Hunan, 410007, China

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Due to the educational nature of the intervention, blinding of students (participants) and instructors (care providers) was not feasible. However, single-blind assessment was enforced: independent faculty outcome assessors and standardized patients (SPs) were fully blinded to participants' group allocations during all evaluation sessions.
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chief Physician, Department of Stomatology

Model Details: A prospective, parallel-group, 1:1 randomized controlled trial comparing a 1-month generative AI-based immersive training intervention (experimental group) with conventional case-based learning (control group).

Study Record Dates

• First Submitted: July 23, 2026
• First Posted: July 28, 2026
• Study Start: March 1, 2026
• Primary Completion: April 1, 2026
• Study Completion: May 31, 2026
• Last Updated: July 28, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: Data will be available immediately following publication of the manuscript.
• Access Criteria: Data will be shared with qualified academic researchers upon reasonable request submitted via email to the corresponding author (libo@hnucm.edu.cn).

Locations

CN (1)