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Related Experiment Video

Updated: Jan 14, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

How Well Can an LLM Chatbot Clarify Queries by Medical Students in Biomedical TBL?

Zheng-Wei Lee¹, Lishan Yang¹, Jessica Yang Huey Ang¹

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

Medical Science Educator

|October 20, 2025

Summary

This study evaluated a large language model (LLM) chatbot in medical education. While helpful for simple questions, the chatbot gave inaccurate answers for complex topics, requiring adjustments for reliable use.

Keywords:

Chatbot evaluation Large Language Models (LLMs)Query clarification SOLO taxonomy Team-Based Learning (TBL)

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Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare

Background:

Team-based learning (TBL) is a pedagogical approach in medical education.
Large language models (LLMs) are increasingly explored for educational support.

Purpose of the Study:

To analyze the performance of an LLM chatbot in a biomedical TBL setting.
To identify the strengths and limitations of LLM chatbots for first-year medical students.

Main Methods:

An LLM chatbot was deployed in a first-year medical student TBL classroom.
Chatbot responses to student queries were analyzed for accuracy and complexity.

Main Results:

The LLM chatbot accurately answered low-complexity biomedical questions.
The chatbot provided incorrect conclusions for high-complexity queries.
The chatbot exhibited a tone of undue confidence in its responses.

Conclusions:

LLM chatbots can assist with basic queries in medical TBL.
Improvements in knowledge accuracy and confidence calibration are essential for effective LLM integration.
Careful implementation is needed to leverage LLM capabilities while mitigating risks in medical education.