An AI Educational Agent for Medical Machine Learning Courses

Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course

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.

Study Overview

• Status: Recruiting
• Conditions: Medical Education; Artificial Intelligence in Medicine
• Intervention / Treatment: Other: KGRAG-based AI Educational Agent System

Detailed Description

Background : Artificial Intelligence (AI) and data mining are becoming essential skills in modern medical and nursing research. However, traditional teaching methods for the graduate-level course "Machine Learning and Data Mining" often struggle to meet the personalized learning needs of students with varying technical backgrounds (e.g., programming, mathematics). To address this, this study introduces a custom-developed AI Educational Agent based on Large Language Models (LLMs) to serve as an intelligent teaching assistant.

Objectives: The primary objective is to evaluate the effectiveness of the AI Agent in improving learning outcomes, practical coding skills, and academic self-efficacy among medical and nursing graduate students. The study also aims to assess the feasibility and student satisfaction of integrating AI agents into the medical curriculum.

Study Design: This is a non-randomized interventional study utilizing a historical control design.

Study Design: This is a non-randomized interventional study utilizing a historical control design.

Experimental Group (Intervention): Students in the 2025-2026 academic year who will receive access to the AI Agent system.

Control Group (Historical): Students from the previous academic cohort (2024-2025) who completed the same curriculum using standard instruction methods without AI support.

Intervention Details: The intervention involves the deployment of an AI Agent system powered by LLMs and Knowledge Graph-based Retrieval-Augmented Generation (KGRAG). The KGRAG framework restricts the AI's responses to a verified knowledge base (course textbooks, lecture slides, and curated code repositories) to minimize "hallucinations" and ensure medical/scientific accuracy. The system includes three specialized functional modules:

Teaching Agent: Functions as a 24/7 tutor, providing concept explanations, summarizing key knowledge points, and offering personalized study plans based on student progress.

Research Agent: Supports research training by assisting with literature review, refining research questions, and optimizing academic writing structures.

Practice Innovation Agent: Facilitates practical skill acquisition by guiding students through code generation, debugging algorithms, and applying machine learning models to real-world medical datasets. The agent employs a Socratic tutoring method to guide problem-solving rather than providing direct answers.

• Study Type: Interventional
• Enrollment (Estimated): 56
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Wei Xia, Phd; Phone Number: 8618823359471; Email: xiaw23@mail.sysu.edu.cn
• Study Contact Backup: Name: Jiebing Luo; Phone Number: 8618885639072; Email: luojiebing2002@163.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Recruiting
      • North Campus of Sun Yat-sen University
      • Contact: Wei Xia, Phd; Phone Number: 8618823359471; Email: xiaw23@mail.sysu.edu.cn
      • Contact: Jie Bing Luo; Phone Number: 8618885639072; Email: luojiebing2002@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

1. Medical graduate students from universities in the Guangdong-Hong Kong-Macao Greater Bay Area;
2. Graduate students who have taken the "Machine Learning and Data Mining" course;
3. Have completed the required prerequisite courses: "Medical Statistics" and "Nursing Research";
4. Capable of operating the AI Educational Agent system normally and willing to undergo relevant teaching interventions and assessments during the study period.

Exclusion Criteria:

1. Unwilling to use the AI education agent system, or refusing to allow the research team to collect their relevant data;
2. Students who cannot commit to the full duration of the course or have known scheduling conflicts that would prevent regular attendance;
3. Students who have previously enrolled in or audited this course in prior academic years to avoid learning effect bias

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: AI Agent Intervention Group; Graduate students enrolled in the "Machine Learning and Data Mining" course during the 2025-2026 academic year. Participants in this group will utilize the custom-developed KGRAG-based AI Educational Agent system throughout the semester. The system includes three modules: a Teaching Agent for concept explanation, a Research Agent for academic writing support, and a Practice Innovation Agent for code generation and debugging

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Composite Academic Performance Score	Assessed through the final cumulative course grade (range: 0-100), which indicates the student's overall mastery of machine learning concepts and applications. The score is calculated based on three weighted components: In-class Assignments (20%): Evaluations of regular assignments submitted via the course platform. Research Progress Paper (40%): A written paper on a free-exploration topic assessing theoretical understanding and research design skills. Group Final Project Presentation (40%): Assessment of a practical project where students present solutions and results based on given medical cases and datasets. Higher scores indicate better academic performance. The experimental group's scores will be compared with the historical control group	After the intervention (at the end of the course, approximately week 3)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Objective Knowledge Acquisition Rate	Evaluated using a structured knowledge assessment embedded in the course surveys. The assessment includes multiple-choice questions covering core concepts , data processing methods , and ethical considerations. The outcome is reported as the percentage of correct responses	After the intervention (at the end of the course, approximately week 3)
Perceived Usefulness and Technology Acceptance	Assessed using the post-course survey based on the Technology Acceptance Model (TAM). Participants rate the helpfulness of the AI Agent for their research and work on a scale of 0 (No help) to 10 (Very helpful)	After the intervention (at the end of the course, approximately week 3)
AI Agent Engagement: Interaction Frequency	Total number of conversations and conversational turns per student, assessed via quantitative analysis of backend system logs to measure student engagement behavior.	At the end of the course (approximately Week 3)
AI Agent Engagement: Temporal Patterns	Comparison of AI agent usage frequency during exam preparation weeks versus regular study weeks, assessed via quantitative analysis of backend system logs.	At the end of the course (approximately Week 3)
AI Agent Engagement: Query Themes	Identification of student query themes through the application of topic modeling algorithms to backend system logs.	At the end of the course (approximately Week 3)

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2025
• Primary Completion (Estimated): March 31, 2026
• Study Completion (Estimated): March 31, 2026

Study Registration Dates

• First Submitted: February 27, 2026
• First Submitted That Met QC Criteria: February 27, 2026
• First Posted (Actual): March 4, 2026

Study Record Updates

• Last Update Posted (Actual): March 5, 2026
• Last Update Submitted That Met QC Criteria: March 3, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Machine Learning; Large Language Models; Educational Agent; KGRAG
• Other Study ID Numbers: L2025SYSU-HL-032; 25XJ0215 (Other Grant/Funding Number: China Association of Higher Education)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The data will be shared one year after the results of the study'are published. The researchers can access the data by contacting the PI at xiaw23@mail.sysu.edu.cn with the research purpose described.
• IPD Sharing Time Frame: After the publication of the study
• IPD Sharing Access Criteria: The researchers can access the data by contacting the PI at xiaw23@mail.sysu.edu.cn with the research purpose described.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No