An interventional study of KGRAG-based AI Educational Agent System in Medical Education and Artificial Intelligence in Medicine, sponsored by Sun Yat-sen University. Recruiting at 1 site in China. Per ClinicalTrials.gov, last updated 2026-03-05.
Sponsored by Sun Yat-sen University · Not applicable, Interventional, and Other
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.
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.
Sun Yat-sen University is the lead sponsor of 1,644 studies on the registry; 602 are open to participants now.
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Exclusion Criteria:
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.
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
Time frame: After the intervention (at the end of the course, approximately week 3)
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
Time frame: 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)
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: At the end of the course (approximately Week 3)
Plan to share: Yes — 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.
Supporting information: Study protocol, Sap, Analytic code
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