## 4 Discussion
However, in practical application, considerable variability exists across institutions and regions in financial capacity and technological readiness. For example, the adoption of subject-specific or management software often requires substantial upfront investment, comprehensive teacher training, and continuous technical support. Institutions with limited funding or underdeveloped digital infrastructure may face significant barriers to implementing these tools effectively. Thus, educational administrators should conduct cost-benefit analyses and infrastructure audits prior to large-scale implementation, striving to balance technological innovation, long-term sustainability, and equitable access. Based on these considerations, this study offers the following recommendations. First, institutions should prioritize investment in subject-specific and management software that directly aligns with the pedagogical goals of physical education, as their synergistic use demonstrates the greatest potential to enhance classroom engagement. Second, digital technology training programs should be provided for all teachers to ensure that each educator is equipped to effectively integrate these tools into instructional practice. In turn, teachers can support students in developing their own digital literacy, helping to reduce disparities and enhance students' perceptions of usefulness and ease of use regarding educational technologies. Third, the implementation of digital tools should be accompanied by ongoing evaluation and feedback mechanisms to monitor their impact on student engagement and guide continuous instructional improvement, thereby maximizing the effectiveness of digital integration in PE classrooms. The results of the SEM analysis indicate that PU, PEU, and ASE serve as significant mediators in the relationship between digital technology usage frequency and classroom engagement. Notably, PU and PEU function as critical mediating variables, further amplifying the positive impact of ASE on engagement. These findings are consistent with previous research [20; 43; 44]. The Technology Acceptance Model (TAM) provides a theoretical explanation for this mechanism [17]. Prior studies have suggested that access to digital infrastructure and technical support is closely related to learners' PU [45], which, in turn, positively influences ASE [20; 46; 47]. In the context of university PE classrooms, when students perceive digital tools as beneficial for improving their learning experience and academic outcomes, and when they find these tools easy to use, their ASE increases. Higher ASE, in turn, fosters positive emotions toward using digital technology and engaging in PE, leading to greater classroom engagement and improved physical activity outcomes [48]. This study's primary strength lies in its methodological innovation. It is the first to integrate machine learning with SEM, not only enabling the precise prediction of classroom engagement but also quantifying the contribution of different digital technology tools. Furthermore, it systematically elucidates the complex mechanisms by which digital technology usage frequency, PU, PEU, and ASE influence engagement. Additionally, this study incorporates a large and geographically diverse sample of university students, enhancing the generalizability and robustness of the findings. Beyond offering valuable policy insights for educational institutions and policymakers regarding the allocation of educational resources and the integration of digital technologies in classrooms, the findings of this study also provide concrete pedagogical implications for PE instructors. In practical PE instruction, teachers must first enhance their digital teaching literacy. This involves not only technical proficiency but also a deeper understanding of the educational potential of digital tools, the ability to analyze and respond to student feedback, and the capacity to effectively align digital technologies with pedagogical objectives.