AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance

This study addresses the need for comprehensive feedback in academic physiotherapy programs. Existing methods often fall short of providing coherent feedback using keywords, leaving a gap in evaluating crucial clinical skills. Introducing iATexF, a Keyword-Based AI system, it offers instant feedback during practical exams, categorized into ’Good’, ’Improvement’, and ’Read more’, aiding students in understanding their performance. Utilizing a Seq2seq framework with diverse LSTM and attention mechanisms, iATexF excels in relevance, achieving a high similarity score of 73% and ROUGE score of 34%. It surpasses both ChatGPT and experts in providing relevant suggestions (80.7%), maintaining an appropriate tone (86.7%), and ensuring a logical structure order (100%). User experience evaluation of the iATexF web application yielded a favorable 92% rating, indicating its usability and effectiveness. This research signifies a significant advancement in educational technology and natural language processing, enhancing physiotherapy training through personalized AI-generated feedback, thereby improving the overall learning experience