[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Hengxu Wang\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":46},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":29,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":4},"100607519","implementation-of-a-blended-online-and-offline-teaching-model-100607519",false,"NCT07189611","Implementation of a Blended Online and Offline Teaching Model","A Study Protocol for Implementing a Blended Online and Offline Teaching Model Based on Generative Artificial Intelligence in the Practical Teaching of Internal Medicine Nursing: a Mixed-methods Study","Inclusion Criteria:\n\n* Nursing major students;\n* Four-year undergraduate students.\n\nExclusion Criteria:\n\n* Students who drop out midway;\n* Students whose absences accumulate to exceed 30% of the total class hours.",true,"ALL","18 Years","25 Years",{"count":21,"type":22},600,"ESTIMATED","INTERVENTIONAL",[25],"NA","This study aims to design, implement, and evaluate a blended online and offline teaching model for Internal Medicine Nursing, integrating generative artificial intelligence (GAI), a virtual simulation platform, card-based exercises, and scenario simulation. The objective is to address key limitations of traditional teaching, including low student engagement, insufficient cultivation of clinical thinking, limited personalized learning, and a disconnect between theory and practice.\n\nA mixed-methods approach will be used. All undergraduate nursing students from the 2024 cohort at Changsha Medical University will be enrolled via convenience sampling as the experimental group to receive the new blended model. The 2023 cohort will serve as the control group, receiving traditional teaching. Quantitative data (course grades, satisfaction questionnaires) and qualitative data (semi-structured interviews) will be collected to comprehensively evaluate the model's effectiveness.\n\nExpected outcomes include improved student mastery of theoretical knowledge, enhanced practical skills and clinical thinking, increased learning interest, and higher teaching satisfaction. The study intends to provide a replicable, scalable innovative solution for nursing education reform, ultimately contributing to the training of high-quality applied nursing talents.\n\nKey problems addressed:\n\nOvercoming single-method teaching and poor interaction through GAI and gamification.\n\nEnhancing clinical thinking and decision-making via dynamic GAI cases and card-based exercises.\n\nProviding personalized learning paths and instant feedback using GAI technology.\n\nBridging the theory-practice gap with high-fidelity virtual and scenario simulations.\n\nImplementing a multi-dimensional evaluation system beyond final exams to assess comprehensive student abilities.",[28],"Generative Artificial Intelligence",[30,31,32,33],"generative artificial intelligence","practical teaching","Internal Medicine Nursing","blended online and offline teaching model","NOT_YET_RECRUITING","2025-09-19",{"date":37,"type":38},"2025-09-24","ACTUAL",{"date":40,"type":22},"2026-01-01",{"date":42,"type":22},"2028-06-01",{"name":44,"class":45},"Hengxu Wang","OTHER",""]