[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100627480":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":19,"centralContacts":20,"locations":30,"responsibleParty":50,"collaborators":19,"id":54,"slug":55,"hasResults":56,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":19,"eligibilityCriteria":60,"healthyVolunteers":61,"sex":62,"minAge":19,"maxAge":19,"enrollmentInfo":63,"targetDuration":19,"studyType":66,"phases":67,"briefSummary":69,"conditions":70,"keywords":73,"overallStatus":33,"whyStopped":19,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"Sun Yat-sen University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"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",[13],"Other: KGRAG-based AI Educational Agent System",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":19},"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\u002F7 access to this system throughout the semester.",[9],null,[21,26],{"name":22,"role":23,"phone":24,"phoneExt":19,"email":25},"Wei Xia, PhD","CONTACT","8618823359471","xiaw23@mail.sysu.edu.cn",{"name":27,"role":23,"phone":28,"phoneExt":19,"email":29},"Jiebing Luo","8618885639072","luojiebing2002@163.com",[31],{"facility":32,"status":33,"city":34,"state":35,"zip":36,"country":37,"countryCode":38,"cosmosGeoPoint":39,"geoPoint":44,"contacts":45},"North Campus of Sun Yat-sen University","RECRUITING","Guangzhou","Guangdong","510000","China","CN",{"type":40,"coordinates":41},"Point",[42,43],113.25,23.11667,{"lat":43,"lon":42},[46,48],{"name":47,"role":23,"phone":24,"phoneExt":19,"email":25},"Wei Xia, Phd",{"name":49,"role":23,"phone":28,"phoneExt":19,"email":29},"Jie Bing Luo",{"type":51,"investigatorFullName":52,"investigatorTitle":53,"investigatorAffiliation":5,"oldNameTitle":19,"oldOrganization":19},"PRINCIPAL_INVESTIGATOR","Wei XIA, PhD","Associate Professor","100627480","an-ai-educational-agent-for-medical-machine-learning-courses-100627480",false,"NCT07449182","An AI Educational Agent for Medical Machine Learning Courses","Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course","Inclusion Criteria:\n\n1. Medical graduate students from universities in the Guangdong-Hong Kong-Macao Greater Bay Area;\n2. Graduate students who have taken the \"Machine Learning and Data Mining\" course;\n3. Have completed the required prerequisite courses: \"Medical Statistics\" and \"Nursing Research\";\n4. Capable of operating the AI Educational Agent system normally and willing to undergo relevant teaching interventions and assessments during the study period.\n\nExclusion Criteria:\n\n1. Unwilling to use the AI education agent system, or refusing to allow the research team to collect their relevant data;\n2. Students who cannot commit to the full duration of the course or have known scheduling conflicts that would prevent regular attendance;\n3. Students who have previously enrolled in or audited this course in prior academic years to avoid learning effect bias",true,"ALL",{"count":64,"type":65},56,"ESTIMATED","INTERVENTIONAL",[68],"NA","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:\n\nDoes the use of an educational AI Agent improve students' academic performance and practical skills in machine learning compared to traditional methods?\n\nDoes the AI intervention enhance students' learning confidence, satisfaction, and cognitive engagement?\n\nResearchers 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.\n\nParticipants will:\n\nUtilize the Teaching Agent for real-time answers to theoretical questions, personalized study planning, and knowledge reinforcement.\n\nEngage with the Research Agent to assist with literature reviews, research design optimization, and academic writing structure.\n\nUse the Practice Innovation Agent for guidance on coding, algorithm debugging, and applying machine learning models to medical data analysis projects.",[71,72],"Medical Education","Artificial Intelligence in Medicine",[74,75,76,77],"Educational Agent","Machine Learning","KGRAG","Large Language Models","2026-03-03",{"date":80,"type":81},"2026-03-05","ACTUAL",{"date":83,"type":81},"2025-05-01",{"date":85,"type":65},"2026-03-31",{"name":5,"class":6},1]