## 4 Discussion
Only through this comprehensive strategy can the full potential of digital technologies be translated into meaningful improvements in teaching quality. Nevertheless, several limitations of this study should be acknowledged. First, the cross-sectional design constrains causal inference and limits the ability to capture temporal dynamics. Future research should consider employing longitudinal designs to track developmental trajectories, or experimental approaches--such as randomized controlled trials--to examine causal relationships among digital technology use, academic self-efficacy, and teaching quality under controlled conditions. Second, the study relies entirely on self reported questionnaire data, which may introduce common method bias and subjectivity. Although validated scales, assurances of anonymity, and robustness checks were employed to mitigate these issues, social desirability bias and other distortions cannot be fully eliminated. Future studies could adopt multi-method approaches, incorporating objective indicators such as academic performance scores, physiological data (e.g., metrics from wearable devices), and structured classroom observation protocols. Moreover, combining student self-reports with teacher evaluations or peer assessments may help reduce single-source bias. Third, this study utilized convenience and snowball sampling, with all participants recruited from Chinese universities. Importantly, over half of the sample comprised first-year undergraduates, potentially introducing experience-related bias and limiting the overall representativeness. To improve the generalizability and applicability of future research, we recommend adopting probability-based random sampling and incorporating cross-cultural comparative designs. Moreover, future studies should aim for more balanced sampling across academic year levels or implement stratified analyses based on year group. Fourth, the valid response rate of 71.84% suggests the potential for survivorship or nonresponse bias. Subsequent research should incorporate strategies for tracking and comparing respondents with non-respondents. Fifth, the analytical models--both machine learning and structural equation modeling--did not account for the differential weighting of variable subdimensions, potentially limiting item-level resolution. Future research should employ more refined measurement modeling techniques to address this limitation. Sixth, although age, gender, academic year, ethnicity, and location were included as control variables, their possible moderating effects were not examined. Future studies should consider multigroup analysis or interaction modeling to explore how these variables might influence the observed associations. Finally, this study focused on only seven types of digital technologies, academic self-efficacy, and teaching quality, using validated yet relatively established measurement instruments. Given the rapid evolution of educational technologies and pedagogical models, future research should consider developing or adopting up-to-date measurement tools that more accurately reflect contemporary digital learning environments. For example, integrating instruments tailored to hybrid instruction, gamification, AI-driven platforms, or wearable technologies could provide a more nuanced understanding of digital engagement and learning outcomes.