Medical EducationNCT07449182
An AI Educational Agent for Medical Machine Learning Courses
A study testing KGRAG-based AI Educational Agent System in people with Medical Education and Artificial Intelligence in Medicine, run by Sun Yat-sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Medical Education, Artificial Intelligence in Medicine
- What is being tested?
- KGRAG-based AI Educational Agent System
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 56 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2025to 31 Mar 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
North Campus of Sun Yat-sen University
Guangzhou, Guangdong, 510000
Wei Xia, Phd, 8618823359471, xiaw23@mail.sysu.edu.cn
Jie Bing Luo, 8618885639072, luojiebing2002@163.com
About this study
Official title: Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course
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?
Read more
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.
Read the detailed description
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.
- Being tested
- KGRAG-based AI Educational Agent System
- Keywords
- Educational Agent, Machine Learning, KGRAG, Large Language Models
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
KGRAG-based AI Educational Agent SystemOther
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 group | Role | What they receive |
|---|---|---|
| AI Agent Intervention Group | Experimental | 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 Receives: Other: KGRAG-based AI Educational Agent System |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: After the intervention (at the end of the course, approximately week 3)
Secondary outcomes (5)
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
Measured: 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)
Measured: 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.
Measured: 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.
Measured: 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.
Measured: At the end of the course (approximately Week 3)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2026
- Study start1 May 2025
- Primary completion31 Mar 2026 (estimated)
- Study completion31 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Medical EducationStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- This is a non-randomized study utilizing a historical control design. The current cohort of students serves as the single experimental group receiving the AI Agent intervention. Their outcomes will be compared to a historical control group (students from the previous academic year) who received standard instruction. Propensity Score Matching (PSM) will be used to control for baseline differences between the two non-concurrent cohorts.
- Masking
- None (open label)
- Enrolment
- 56 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wei XIA, PhD, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07449182
- Sponsor's protocol ID
- L2025SYSU-HL-032
- Other IDs
- 25XJ0215
- Status last verified
- Feb 2026
- First submitted
- 27 Feb 2026
- Last updated
- 5 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Study at a glance
From the registry record, last updated by the sponsor on 5 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course
- ClinicalTrials.gov ID
- NCT07449182
- Acronym / sponsor ID
- L2025SYSU-HL-032
- Keywords
- Educational Agent, Machine Learning, KGRAG, Large Language Models
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 56 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 1 May 2025 (actual)
- Primary completion
- 31 Mar 2026 (estimated)
- Study completion
- 31 Mar 2026 (estimated)
- First posted
- 4 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- This is a non-randomized study utilizing a historical control design. The current cohort of students serves as the single experimental group receiving the AI Agent intervention. Their outcomes will be compared to a historical control group (students from the previous academic year) who received standard instruction. Propensity Score Matching (PSM) will be used to control for baseline differences between the two non-concurrent cohorts.
| Arm | Type | Receives |
|---|---|---|
| AI Agent Intervention Group | Experimental | Other: KGRAG-based AI Educational Agent SystemGraduate 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 |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| KGRAG-based AI Educational Agent SystemThe 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. | Other | AI Agent Intervention Group |
Primary outcome
Composite Academic Performance Score
Time frame: 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
5 secondary outcomes
Objective Knowledge Acquisition Rate
Time frame: After the intervention (at the end of the course, approximately week 3)
Perceived Usefulness and Technology Acceptance
Time frame: After the intervention (at the end of the course, approximately week 3)
AI Agent Engagement: Interaction Frequency
Time frame: At the end of the course (approximately Week 3)
AI Agent Engagement: Temporal Patterns
Time frame: At the end of the course (approximately Week 3)
AI Agent Engagement: Query Themes
Time frame: At the end of the course (approximately Week 3)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Mar 2026.
- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing KGRAG-based AI Educational Agent System in people with Medical Education and Artificial Intelligence in Medicine, run by Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wei Xia, PhD (8618823359471, xiaw23@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Composite Academic Performance Score (time frame: After the intervention (at the end of the course, approximately week 3)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Mar 2026.