[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100607519":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":21,"locations":20,"responsibleParty":27,"collaborators":20,"id":31,"slug":32,"hasResults":33,"nctId":34,"briefTitle":35,"officialTitle":36,"acronym":20,"eligibilityCriteria":37,"healthyVolunteers":38,"sex":39,"minAge":40,"maxAge":41,"enrollmentInfo":42,"targetDuration":20,"studyType":45,"phases":46,"briefSummary":48,"conditions":49,"keywords":51,"overallStatus":56,"whyStopped":20,"lastUpdateSubmitDate":57,"lastUpdatePostDateStruct":58,"startDateStruct":61,"completionDateStruct":63,"leadSponsor":65,"locationsCount":20},{"fullName":5,"class":6},"Changsha Medical University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Experimental group","EXPERIMENTAL","(2) Experimental Group Teaching Implementation Process: a blended online and offline teaching model based on generative artificial intelligence\n\n① Pre-class Preview: Students join the teaching QQ group and Learning Terminal group before class, complete the learning of online resources on the Learning Terminal platform, and perform virtual simulation experiments. ② In-class Implementation: Teaching is conducted in small groups. Each class is divided into 4 small groups, with 4-5 students forming one team for card-based desktop exercise teaching and scenario simulation teaching, each session lasting 2 class hours.③ Post-class Review: Students use generative AI (Deepseek) for knowledge consolidation and to access new technologies and research advancements related to the course content.",[13],"Behavioral: A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":20},"BEHAVIORAL","A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence","This study will employ a convergent mixed-methods design. Participants will be convenience-sampled undergraduate nursing students from the 2024 cohort (intervention group) and the 2023 cohort (control group) at Changsha Medical University. The intervention group will experience the new blended model, which includes: 1) Optimizing a GAI-assisted clinical case library with progressive scenarios; 2) Utilizing online resources (Learning Terminal platform, virtual simulation experiments with an AI assistant); 3) Engaging in offline interactive sessions (card-based desktop deduction games and scenario simulations). The control group will receive traditional teaching methods. Quantitative data will include course scores (theoretical knowledge and practical skills) and teaching satisfaction questionnaires. Qualitative data will be collected via semi-structured interviews to explore students' experiences deeply.",[9],null,[22],{"name":23,"role":24,"phone":25,"phoneExt":20,"email":26},"hengxu wang","CONTACT","+86-15575503185","1543980936@qq.com",{"type":28,"investigatorFullName":29,"investigatorTitle":30,"investigatorAffiliation":5,"oldNameTitle":20,"oldOrganization":20},"SPONSOR_INVESTIGATOR","Hengxu Wang","Staff Nurse","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":43,"type":44},600,"ESTIMATED","INTERVENTIONAL",[47],"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.",[50],"Generative Artificial Intelligence",[52,53,54,55],"generative artificial intelligence","practical teaching","Internal Medicine Nursing","blended online and offline teaching model","NOT_YET_RECRUITING","2025-09-19",{"date":59,"type":60},"2025-09-24","ACTUAL",{"date":62,"type":44},"2026-01-01",{"date":64,"type":44},"2028-06-01",{"name":29,"class":6}]