Implementation of a Blended Online and Offline Teaching Model

NCT07189611 | Not Yet Recruiting DMC

• 1 other identifier: X2025046
• Study Type: interventional
• Target: 600
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Generative Artificial Intelligence

Keywords

generative artificial intelligence; practical teaching; Internal Medicine Nursing; blended online and offline teaching model

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  At the end of the 6-month course.

Secondary Outcomes (2)

• Online and Offline Teaching Effect Evaluation

  At the end of the 6-month course.

• Qualitative Interviews

  Within one month after completion of the 6-month course.

Study Arms (1)

Experimental group

EXPERIMENTAL

(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

Interventions

A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence BEHAVIORAL

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.

Experimental group

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Nursing major students;
• Four-year undergraduate students.

You may not qualify if:

• Students who drop out midway;
• Students whose absences accumulate to exceed 30% of the total class hours.

Sponsors & Collaborators

• Hengxu Wang: lead

Central Study Contacts

hengxu wang

CONTACT

+86-15575503185; 1543980936@qq.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Staff Nurse

Study Record Dates

• First Submitted: August 27, 2025
• First Posted: September 24, 2025
• Study Start: January 1, 2026
• Primary Completion (Estimated): January 1, 2028
• Study Completion (Estimated): June 1, 2028
• Last Updated: September 24, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share