ENTERPRISE AI ANALYSIS
Effects of Artificial Intelligence Based Physiotherapy Educational Approach in Developing Clinical Reasoning Skills: A Randomized Controlled Trial
This study investigates the impact of an AI-assisted problem-based learning (AI-PBL) approach on theoretical knowledge, clinical competence, and AI self-efficacy in undergraduate physiotherapy students. Comparing AI-PBL with traditional PBL, findings suggest that supervised, structured use of generative AI enhances sustained learning and digital self-efficacy without increasing behavioral risks. The AI-PBL approach fosters active reflection and deeper academic engagement, offering a promising direction for digital innovation in physiotherapy education.
Key AI Integration Metrics
Understanding the measurable impact of AI-driven educational strategies on key learning outcomes.
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Knowledge Retention (AI-PBL)
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AI Self-Efficacy Gains (AI-PBL)
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Avg. Reading Motivation Gain (AI-PBL)
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Internet Addiction Increase (AI-PBL)
Deep Analysis & Enterprise Applications
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AI's Impact on Learning Outcomes in Physiotherapy Education
3.14
Cohen's d: Knowledge Retention (AI-PBL vs. PBLG)
The AI-PBL group demonstrated an exceptionally large improvement from immediate post-test to 2-week retention (Cohen's d=3.14), indicating enhanced understanding and consolidation. This sustained learning may stem from interactive AI experiences promoting questioning and self-directed learning.
1.78
Cohen's d: AI Self-Efficacy Gains (AI-PBL vs. PBLG)
AI-PBL participants showed a very large and statistically significant increase in perceived competence and confidence in using AI-based technologies (Cohen's d=1.78).
| Feature | AI-PBL Group Insights | Traditional PBL Group Insights |
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| Clinical Competence (Mini-CEX) |
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| Reading Motivation (ARMS) |
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While AI-PBL showed numerically higher scores for clinical competence (Mini-CEX), the difference was not statistically significant (p=0.236). Both groups improved in reading motivation, but the AI-PBL group showed greater gains (∆X=12.7) compared to traditional PBL (∆X=9.8), although not statistically significant between groups (Cohen's d=0.24).
AI-PBL Instructional Structure
Structured Orientation to ChatGPT-4.0
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AI-assisted Case Scenario Development
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Evidence-informed Evaluation & Treatment Plan
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Instructor Review & Feedback
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Simulated Patient Practice
Transforming Healthcare Learning with AI
Enhancing Physiotherapy Education with Generative AI
This study demonstrates that supervised, structured integration of generative AI like ChatGPT into physiotherapy problem-based learning (PBL) can significantly enhance theoretical knowledge retention and AI self-efficacy. By enabling students to create simulated patient scenarios, refine clinical hypotheses, and develop treatment plans with AI assistance, the approach fosters active reflection, self-directed learning, and deeper academic engagement. This model supports critical thinking and interpretive skills, offering a promising direction for digital innovation in health professions education.
The findings emphasize the potential of AI to serve as a valuable pedagogical aid, promoting clinical reasoning and facilitating interactive, case-based learning when integrated ethically and with academic supervision. This cultivation of professional dialogue grounded in trust and empathy is crucial for future healthcare professionals.
Broader AI Integration Steps for Healthcare Education
Assess Current Curricula for AI Gaps
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Develop AI Literacy Training for Educators & Students
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Integrate AI Tools into PBL Frameworks (Supervised)
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Monitor Learning Outcomes & Behavioral Engagement
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Refine & Scale AI-Enhanced Educational Strategies
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Estimated Annual Savings
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