Implementation of a Blended Online and Offline Teaching Model

NCT ID: NCT07189611

Last Updated: 2025-09-24

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 600 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2026-01-01
• Study Completion Date: 2028-06-01

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.

Detailed Description

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.

Conditions

Generative Artificial Intelligence

Study Design

• Allocation Method: NA
• Intervention Model: SEQUENTIAL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Experimental group

(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.

Group Type: EXPERIMENTAL

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

Intervention Type: 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.

Interventions

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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 25 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Hengxu Wang

OTHER

Sponsor Role: lead

Responsible Party

Hengxu Wang

Staff Nurse

Responsibility Role: SPONSOR_INVESTIGATOR

Central Contacts

hengxu wang

Role: CONTACT

1543980936@qq.com

+86-15575503185

Provided Documents

Document Type: Informed Consent Form

View Document

Other Identifiers

X2025046

Identifier Type: -

Identifier Source: org_study_id