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Implementation of a Blended Online and Offline Teaching Model

NCT07189611 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 600

Last updated 2025-09-24

No results posted yet for this study

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

Interventions

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.

Sponsors & Collaborators

  • Hengxu Wang

    lead OTHER

Study Design

Allocation
NA
Purpose
OTHER
Masking
NONE
Model
SEQUENTIAL

Eligibility

Min Age
18 Years
Max Age
25 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-01-01
Primary Completion
2028-01-01
Completion
2028-06-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07189611 on ClinicalTrials.gov