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An AI Educational Agent for Medical Machine Learning Courses

NCT07449182 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 56

Last updated 2026-03-05

No results posted yet for this study

Summary

The goal of this interventional study is to evaluate the effectiveness of a Large Language Model (LLM)-based educational AI Agent in graduate students (Masters and PhD) specializing in medicine or nursing who are enrolled in the "Machine Learning and Data Mining" course. The main questions it aims to answer are:

Does the use of an educational AI Agent improve students' academic performance and practical skills in machine learning compared to traditional methods?

Does the AI intervention enhance students' learning confidence, satisfaction, and cognitive engagement?

Researchers will compare students currently using the AI Agent (experimental group) to a historical control group (students from the previous cohort who did not use the AI tool) to see if the AI-assisted learning model leads to significantly higher learning achievements and better educational experiences.

Participants will:

Utilize the Teaching Agent for real-time answers to theoretical questions, personalized study planning, and knowledge reinforcement.

Engage with the Research Agent to assist with literature reviews, research design optimization, and academic writing structure.

Use the Practice Innovation Agent for guidance on coding, algorithm debugging, and applying machine learning models to medical data analysis projects.

Conditions

  • Medical Education
  • Artificial Intelligence in Medicine

Interventions

OTHER

KGRAG-based AI Educational Agent System

The intervention involves a custom-developed AI educational system powered by Large Language Models (LLMs) and Knowledge Graph-based Retrieval-Augmented Generation (KGRAG) technology. The system comprises three specialized agents to support self-directed learning: 1. Teaching Agent: Provides real-time concept explanations, personalized study plans, and knowledge reinforcement based on the course curriculum. 2. Research Agent: Assists with literature review, research question refinement, and academic writing structure. 3. Practice Innovation Agent: Guides students through code generation, algorithm debugging, and data mining projects using Socratic tutoring methods to foster problem-solving skills. Participants have 24/7 access to this system throughout the semester.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Study Design

Allocation
NA
Purpose
OTHER
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-05-01
Primary Completion
2026-03-31
Completion
2026-03-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07449182 on ClinicalTrials.gov