An AI Educational Agent for Medical Machine Learning Courses
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorSun Yat-sen University
About this trial
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 criteria
Qualifiers
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.
Disqualifiers
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
Trial design
Treatments tested in this trial
- KGRAG-based AI Educational Agent System
Treatment groups
56 Participants
are divided into 1 treatment groupSponsors and collaborators
Source ClinicalTrials.gov