Evaluation of an AI-Assisted Learning Approach for Enhancing Clinical Knowledge in Respiratory Therapy Students

Critical care requires timely patient assessment, clinical judgment, and appropriate intervention, yet respiratory therapy students may have limited opportunities to experience complex clinical situations before entering clinical training. Traditional teaching methods are often based on lectures, case discussions, and demonstrations, and may be limited by available facilities, equipment, and clinical cases. AI-based virtual human simulation provides a safe and repeatable environment in which students can practice clinical decision-making and receive immediate feedback. This study will recruit eligible respiratory therapy students who voluntarily participate in AI-based critical care simulation training and will compare pre- and post-training assessments of learning outcomes, self-efficacy, and learning experience. The training is expected to improve students' critical care knowledge, clinical reasoning, confidence, and learning engagement, and the findings may provide a reference for future pre-clinical training and AI-assisted respiratory therapy education.