An AI Educational Agent for Medical Machine Learning Courses

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.

Inclusion criteria

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

Exclusion criteria

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