Current applications and outcomes of AI-driven adaptive learning systems in physical rehabilitation science...

Rationale Integrating artificial intelligence (AI) into education has introduced transformative possibilities, particularly through adaptive learning systems. Rehabilitation science education stands to benefit significantly from the integration of AI-driven adaptive learning systems. However, the application of these technologies remains underexplored. Understanding the current applications and outcomes of AI-driven adaptive learning in broader healthcare education can provide valuable insights into how these approaches can be effectively adapted to enhance multimodal case-based learning in Rehabilitation Science education. Methods The scoping review is based on the Joanne Briggs Institute (JBI) framework. It is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRIMSA-ScR). A comprehensive search strategy will be used to find relevant papers in Scopus, PubMed, CINAHL, Education Resources Information Center (ERIC), Association for Computing Machinery (ACM), ProQuest Education Journal, Web of Science, ProQuest Dissertations & Theses Global, and IEEE Digital Library. This review will include all types of studies that describe or evaluate our outcomes of interest: AI models used, learning and teaching methods, effective implementation, outcomes, and challenges of ALS's in rehabilitation health science education. Data will be extracted using a pre-piloted data extraction sheet and synthesized narratively to identify themes and patterns. Discussion This scoping review will synthesize the applications of AI models in rehabilitation science education. It will provide evidence for educators, healthcare professionals, and policymakers to incorporate AI into educational curricula effectively. The protocol is registered on Open Science Framework registries at https://osf.io/e46s3.