BEYOND ONE-SIZE-FITS-ALL: REIMAGINING PERSONALIZED AND ADAPTIVE LEARNING IN PHYSIOTHERAPY EDUCATION

Abstract

In contemporary physiotherapy education, personalized and adaptive learning have become popular strategies to overcome the drawbacks of conventional one-size-fits-all teaching approaches. Learner-centred instructional frameworks that adjust to individual needs, learning speed, and skills have been made possible by advancements in digital technologies, such as artificial intelligence (AI), learning analytics, and simulation-based platforms. While adaptive learning employs data-driven algorithms to dynamically alter content delivery and evaluation processes, personalized learning focuses on customizing educational experiences to students' preferences, prior knowledge, and clinical interests. These methods are particularly significant in physiotherapy education, where it is essential to integrate academic knowledge, clinical reasoning, and practical skills. By providing real-time feedback, identifying knowledge gaps, and promoting competency-based development, adaptive learning systems can enhance learning outcomes and clinical readiness. Despite these benefits, issues with teacher preparation, ethics, data privacy, and technology accessibility continue to be major challenges. The methods, uses, and results of customized and adaptive learning in physiotherapy education are examined in this chapter. It also emphasizes the necessity of combining innovative methods with conventional teaching techniques to provide comprehensive, equitable, and effective education.