Empowering college physical education: AI-driven training, teaching, and intelligent information processing
Abstract
The current status, methods utilized for Physical Education Training and Teaching System for College Students, and difficulties during information processing are all investigated in this comprehensive study. We compiled 130 empirical research on Artificial Intelligence-based Physical Education (AIPE) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses are analysed. Research shows that AI may improve health tracking, individualized training, and analysis of sporting performance. There is a lot of promise in AIPE for individualized lessons, immediate feedback, varied classroom settings, and evaluation. The problems arise when dealing with technological dependability, privacy concerns, and the need for instructor assistance. These results give light on important questions for future AIPE. This research delves into the process of creating and launching an all-encompassing educational platform that makes use of AI and data processing methods. Our system’s goals include improving the quality of instruction, tailoring feedback to each student, and enhancing the overall learning experience. AI Algorithms powered by artificial intelligence help us shift through student test scores, identify knowledge gaps, and modify lessons appropriately. This makes sure that the curriculum meets each student’s needs. Practical exercises, quizzes, and assignments get immediate feedback through the system. It uses natural language processing (NLP) to analyse student answers, find misunderstandings, and provide help for fixing them. The system personalizes learning routes according to students’ choices, learning styles, and progress. It suggests further reading, interactive games, and group assignments. Through the automation of administrative activities, generation of analytics reports, and suggestion of pedagogical changes, the system aids instructors. It makes it easier for students and instructors to talk to one another. Data protection, overcoming AI biases, and getting teachers on board with tech-enhanced lessons are all obstacles. Future studies should aim to improve the system, confirm its efficacy, and encourage its implementation across educational institutions. Finally, there is great potential for improving higher education with an AI-based training and teaching system with strong data processing skills. Students and teachers may reap the advantages of a technologically enhanced, ever-changing learning environment.
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