Examining the Role of Artifical Intelligence in Enhancing Adapted Physical Education (APE) in Universities in Harbin, Heilongjiang, China

Abstract

This study investigated the relationships between Physical Disabilities of Chinese students in APE, Intelligent Health Information Systems (IHIS), and various AI technologies including Machine Learning (ML), Artificial Neural Networks (ANN), Natural Language Processing (NLP), Deep Learning (DL), and Bayesian Networks (BN) to determine how AI can enhance adaptive physical education effectiveness. A quantitative sequential explanatory design was employed with 431 participants from 30 universities across Heilongjiang Province. Data collection utilized structured questionnaires assessed through Structural Equation Modeling (SEM) with maximum likelihood estimation. The study examined five distinct AI technological domains and their implementation through IHIS as a mediating mechanism. A strong positive effect was demonstrated between Physical Disabilities and IHIS (β = 0.924, p < .001).. Significant improvements were observed across five key barrier domains: physical accessibility (43.7%), communication barriers (51.2%), instructional differentiation (47.6%), assessment adaptability (39.8%), and social inclusion (32.4%). AI technologies, when properly implemented through robust health information systems, substantially enhance adaptive physical education for students with disabilities. IHIS serves as a critical mediating mechanism that translates accommodation needs into effective technological implementations. The findings support a comprehensive technological ecosystem approach rather than isolated implementations, providing evidence-based guidance for educational institutions seeking to enhance inclusive education capabilities while maintaining instructional effectiveness.