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Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System

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Large language model-driven Socratic questioning for endodontic case analysis: A randomised controlled trial.

Wenjing Liu¹, Xueying Huang¹, Minkang Zhan¹

¹Stomatological Hospital, Southern Medical University, Guangzhou, China.

International Endodontic Journal

|July 9, 2026

Summary

A large language model (LLM)-based Socratic questioning system effectively trained dental students in endodontic case analysis. This AI tool achieved similar outcomes to traditional case-based learning (CBL) while significantly reducing faculty teaching time.

Keywords:

artificial intelligence case‐based learning clinical reasoning endodontic education large language models socratic questioning

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Last Updated: Jul 12, 2026

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Published on: February 23, 2024

Area of Science:

Dental Education
Artificial Intelligence in Medicine
Endodontics

Background:

Endodontic education faces challenges in providing sufficient clinical reasoning practice due to resource limitations.
Case-based learning (CBL) is a valuable method but can be resource-intensive for faculty.
Developing innovative educational tools is crucial for enhancing structured clinical reasoning skills.

Purpose of the Study:

To develop and evaluate a large language model (LLM)-based Socratic questioning system for endodontic case-analysis training.
To compare the feasibility, educational outcomes, learner experience, and faculty time of the LLM system against traditional CBL.
To assess the potential of AI to supplement existing endodontic educational resources.

Main Methods:

A randomized controlled trial involving 79 third-year dental students comparing an AI-guided group (n=40) with a CBL group (n=39).
The AI system utilized a Socratic questioning engine, case-analysis scoring, and adaptive recommendations.
Both groups underwent a one-hour intervention before simulated case assessment; outcomes included scores and faculty time.

Main Results:

Both AI and CBL groups showed significant within-group improvement in case-analysis scores.
No statistically significant difference was found in post-training total scores or score improvement between the AI and CBL groups.
The AI intervention required substantially less direct faculty teaching time per student (0.026 hours vs. 0.45 hours for CBL).

Conclusions:

An LLM-based Socratic questioning system is feasible for endodontic simulated case-analysis training.
The AI system demonstrated comparable short-term educational outcomes to faculty-led CBL.
The LLM system offers a valuable supplementary tool to address resource constraints in endodontic education by reducing faculty workload.