Clinical Research Directory

MAGICS - Multi-Agent Generative AI in Clinical Simulation for Clinical Deterioration Training

This study compares the effectiveness of a Generative AI-integrated blended simulation programme and high-fidelity simulation alone on nursing students' clinical reasoning and learning experience during deterioration training

AI-Powered Virtual Human Simulation in Students

This study aims to investigate the effects of artificial intelligence-based virtual human simulation training on students' learning outcomes in critical care knowledge and clinical skills, as well as its effects on learning motivation, clinical confidence, learning satisfaction, and critical thinking disposition.

The Effect of an Artificial Intelligence-Based Branching Path Simulation for First Aid Education on Nursing Students' Knowledge, Satisfaction, and Motivation

Background: Rapid advancements in educational technology are necessitating a renewal of teaching approaches in nursing education. Artificial intelligence-based branching-path simulations, which allow students to make choices at decision points and see the outcome of their choices through instant feedback, are emerging as a new teaching tool that can facilitate students' active participation in clinical decision-making processes. Objectives: This study was conducted to evaluate the effect of an AI-based branching-path simulation on nursing students' level of knowledge regarding first aid for injuries, course satisfaction, and learning motivation. Methods: This study was conducted as a randomized controlled trial. The study was completed at the nursing department of a public university with the participation of 164 first-year nursing students (intervention group n=84, control group n=80). Students in the intervention group were administered an AI-based branching-path simulation consisting of three modules-penetrating abdominal injury, head trauma, and penetrating chest injury-following the first aid course for injuries, while students in the control group were trained using traditional question-and-answer and case discussion methods at the end of the same course. Data were collected using a student information form, a knowledge test developed by the researchers, and a course content evaluation form.

AI-Assisted Adaptive Simulation in Physiology Education

This randomized controlled trial evaluated whether an AI-assisted, rule-based adaptive screen-based simulation module could improve physiology learning outcomes among undergraduate health science students compared with conventional instruction. A total of 672 students from Physiotherapy, Occupational Therapy, Nursing, and Allied Health Sciences were randomly assigned in a 1:1 ratio to either the adaptive simulation group or the conventional teaching group. The intervention used web-based clinical physiology cases with algorithm-supported case sequencing, automated formative feedback, and structured faculty-led debriefing, while the control group received standard lectures, textbook reading, tutorial sessions, and laboratory practicals. The primary outcomes were physiological knowledge and reasoning ability, and the secondary outcomes were conceptual understanding, engagement, cognitive load, and academic self-efficacy. Assessments were performed at baseline, immediately after the 12-week intervention, and again at four-week follow-up.