AI teaching assistant for a graduate medical machine learning course
Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course
This project will test whether an AI teaching assistant helps medical and nursing graduate students in a Machine Learning and Data Mining course improve their grades, coding skills, and confidence.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 56 (estimated) |
| Sex | All |
| Sponsor | Sun Yat-sen University Academic / other |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT07449182 on ClinicalTrials.gov |
What this trial studies
This non-randomized interventional study will deploy a Knowledge Graph–enhanced LLM-based educational agent as a teaching assistant for Masters and PhD students enrolled in a Machine Learning and Data Mining course at Sun Yat-sen University. The 2025–2026 cohort using the AI agent will be compared to a historical 2024–2025 cohort that received standard instruction, with primary outcomes including academic performance, practical coding ability, and academic self-efficacy. Secondary outcomes include student satisfaction, cognitive engagement, and feasibility of integrating the agent into the curriculum. The agent will provide real-time answers, personalized study plans, and code support throughout the course.
Who should consider this trial
Good fit: Ideal participants are Masters or PhD students in medicine or nursing from universities in the Guangdong‑Hong Kong‑Macao Greater Bay Area who are enrolled in the Machine Learning and Data Mining course, have completed Medical Statistics and Nursing Research, and can operate the AI system.
Not a fit: Students unwilling to use the AI agent, those lacking required prerequisites or regular attendance, or students who previously completed the course are unlikely to gain benefit from the intervention.
Why it matters
Potential benefit: If successful, this approach could speed personalized learning, raise practical machine-learning skills and confidence among graduate medical and nursing students, and be scaled to other courses.
How similar studies have performed: Prior educational research shows LLM-based tutors can improve learning in some settings, but combining LLMs with knowledge-graph retrieval for graduate medical machine-learning instruction is relatively novel and has limited direct evidence.
Eligibility criteria
Show full inclusion / exclusion criteria
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
Where this trial is running
Guangzhou, Guangdong
- North Campus of Sun Yat-sen University — Guangzhou, Guangdong, China (Recruiting)
Study contacts
- Study coordinator: Wei Xia, PhD
- Email: xiaw23@mail.sysu.edu.cn
- Phone: 8618823359471
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.