AI Educational Support Framework for At-Risk Media Students

In this study, we developed an AI framework for at-risk media students to evaluate the impact of AI tools on student engagement and academic performance. Our specific focus was on at-risk media students enrolled in Egyptian universities. The data of 300 at-risk media students was collected through an online survey. The analysis of data was performed through Partial Least Squares Structural Equation Modeling (PLS-SEM) that ensured the robustness of the proposed AI-framework. The results validate the significance of AI framework as all proposed hypothesized relationships were supported. The findings demonstrate that the facilitating conditions, learning motivation, and students’ engagement exert a strong impact on AI framework for at-risk media students. This research enriches the current theoretical understanding of AI tools adoption for at-risk media students and provides directions to developers and educators to design user-friendly features. The proposed framework is an effective tool that supports students leaning, satisfaction motivation, and academic performance.