An AI Educational Agent for Medical Machine Learning Courses

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

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.

Eligibility

Primary outcome measure(s)

• Composite Academic Performance Score — After the intervention (at the end of the course, approximately week 3)
  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

Trial sites (1)

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