An interventional study of A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence in Generative Artificial Intelligence, sponsored by Hengxu Wang. Not yet recruiting. Open to participants aged 18 Years to 25 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-09-24.
Sponsored by Hengxu Wang · Not applicable, Interventional, and Other
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.
A 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.
Expected 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.
Key problems addressed:
Overcoming single-method teaching and poor interaction through GAI and gamification.
Enhancing clinical thinking and decision-making via dynamic GAI cases and card-based exercises.
Providing personalized learning paths and instant feedback using GAI technology.
Bridging the theory-practice gap with high-fidelity virtual and scenario simulations.
Implementing a multi-dimensional evaluation system beyond final exams to assess comprehensive student abilities.
This study protocol describes the development, implementation, and evaluation of a blended online and offline teaching model integrated with generative artificial intelligence (GAI) for practical teaching in Internal Medicine Nursing. The model combines a GAI-optimized clinical case library, a virtual simulation platform, card-based desktop exercises, and scenario simulation teaching.
The clinical case library will be developed using GAI to generate progressive, multi-stage cases reflecting real clinical progression (e.g., from COPD to Cor Pulmonale), each containing 2-3 stages designed to train clinical reasoning and decision-making. Online teaching resources will include a Learning Terminal-based course covering nine internal medicine systems, with electronic courseware, assessments, and discussion forums. The existing virtual simulation platform will be enhanced with a GAI-based Q\&A assistant to support knowledge acquisition and operational training. Dedicated online learning groups will facilitate communication.
Offline teaching will incorporate card-based desktop exercises and high-fidelity scenario simulations. The card game includes five card types: Patient Information, Nursing Goal, Nursing Intervention, Emergency Situation, and Assessment \& Feedback. Scenarios are derived from the GAI case library and involve standardized patients and high-fidelity simulators to replicate clinical environments.
The model will be implemented using a mixed-methods design. The experimental group (2024 undergraduate nursing cohort) will receive the blended model, while the control group (2023 cohort) will receive traditional teaching. Evaluation includes quantitative metrics (theory and practical exam scores, teaching satisfaction surveys) and qualitative methods (semi-structured interviews with the experimental group). Course scores are weighted 60% for theory and 40% for practical skills, the latter comprising case analysis, emergency drills, virtual simulation performance, and online course results. A multidimensional evaluation mechanism involving students, teachers, and expert supervisors will be established.
The teaching team consists of 8 full-time instructors, 4 clinical teachers, and 4 training center staff. Lessons learned from the mixed-methods evaluation will be used to refine and promote the teaching model.
This is the only study on the registry with Hengxu Wang as lead sponsor.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
(2) Experimental Group Teaching Implementation Process: a blended online and offline teaching model based on generative artificial intelligence ① 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.
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.
Course Scores
The total course score is a composite measure evaluating overall academic performance. It comprises two components: a theoretical knowledge score (assessed via a closed-book examination, scored out of 100 points) and a practical skill assessment score (evaluated through case analysis, emergency drill simulations, virtual simulation performance, and online course participation, each contributing 20% to the practical score, which is also scaled to 100 points). The final total course score is calculated by weighting the theoretical score at 60% and the practical score at 40%, resulting in a composite value out of 100.
Time frame: At the end of the 6-month course.
Teaching Satisfaction Score
Teaching satisfaction will be measured using a validated evaluation questionnaire developed based on a review of relevant literature, research group discussions, and consultation with nursing education experts. The questionnaire produces a quantitative satisfaction score.
Time frame: At the end of the 6-month course.
Online and Offline Teaching Effect Evaluation
A multidimensional teaching effectiveness score will be calculated based on a digital combined evaluation system. This system incorporates standardized quantitative evaluations from three sources: student self-assessment (using a structured rating scale), teacher and small team self-assessment (using a structured rating scale), and expert supervision group assessment (using a structured observation rubric). These assessments are designed to use a common unit of measure (points on a standardized scale). The scores from these three components will be aggregated using a pre-defined weighting formula to produce a single composite teaching effectiveness score.
Time frame: At the end of the 6-month course.
Qualitative Interviews
Thematic analysis of semi-structured individual interviews conducted with participants from the experimental group. This qualitative measure will identify, analyze, and report patterns (themes) concerning the perceived effectiveness, learning experiences, strengths, and limitations of the blended teaching model. The results will be presented as a summary of key themes and insights, not as a numerical score.
Time frame: Within one month after completion of the 6-month course.
No study locations are listed for this record.
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Plan to share: No — Protection of Participant Privacy and Confidentiality: Our primary ethical obligation is to protect the privacy and confidentiality of our student participants. Informed Consent Limitations: The informed consent process for this study, approved by the institutional ethics review board, explicitly outlines how the collected data will be used (i.e., for aggregate analysis to evaluate teaching effectiveness within the confines of this research project). Nature of the Data and High Risk of Re-identification: The qualitative interview data are particularly sensitive.
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