Implementation of a Blended Online and Offline Teaching Model

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Поведенческое 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 as

Первичные конечные точки

• Course Scores [Срок оценки: At the end of the 6-month course.]
• Teaching Satisfaction Score [Срок оценки: At the end of the 6-month course.]

Вторичные конечные точки (2)

• Online and Offline Teaching Effect Evaluation [Срок оценки: At the end of the 6-month course.]
• Qualitative Interviews [Срок оценки: Within one month after completion of the 6-month course.]

Критерии участия

Критерии включения

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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Не применимо

Модель

Последовательный дизайн

Маскирование

Открытое

Основная цель

Другое

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Идентификаторы

NCT: NCT07189611 · X2025046

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗