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Physiotherapy education is evolving as Artificial Intelligence (AI) reshapes traditional teaching methods. This thesis presents an AI-powered chatbot that enhances physiotherapy students' learning by offering personalized, adaptive support. Utilizing Natural Language Processing (NLP) and Machine Learning (ML), the chatbot delivers real-time feedback, interactive modules, and immediate assistance in both theoretical and practical contexts.
The AI chatbot addresses key challenges in modern healthcare education, such as the need for customized learning, flexible access, and practical experience in digital environments. It adapts to individual student needs, providing tailored pathways that align with their pace and knowledge gaps. By offering virtual clinical simulations, it enables students to practice patient assessments, diagnosis, and treatment planning, overcoming limitations of traditional clinical placements.
This research demonstrates the chatbot’s ability to significantly improve learning outcomes through continuous access to high-quality, consistent educational resources. Students engage in scenario-based learning, reinforcing critical thinking and clinical decision-making skills. The chatbot simulates real-world patient interactions, providing instant feedback and filling the gap left by limited mentorship opportunities.
The chatbot’s development followed a rigorous, iterative process, refining its NLP capabilities and user interface for maximum engagement. Key contributions include not only advancements in physiotherapy education but also broader implications for healthcare training as AI continues to evolve.
Despite its clear advantages—enhanced accessibility, flexibility, and engagement—the study also addresses challenges such as technological literacy, data privacy, and user adoption. Ethical considerations and user feedback were integral to its design, ensuring responsible AI development in education.