<![CDATA[The rapid integration of Artificial Intelligence (AI) in higher education has the potential to transform learning, yet access to technology does not guarantee active student participation2. Concrete evidence regarding the specific impact of AI features on psychological engagement remains limited. This study aims to examine the structural relationship between AI features (Usage, Personalization, and Feedback) and Student Engagement, specifically investigating the mediating role of Technology Engagement3. Methods: This study employed a quantitative approach with a non-experimental cross-sectional design4. Data were collected from 71 undergraduate students in Eastern Indonesia, predominantly from information technology majors5. The structural model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software to test direct and indirect effects6. Results: The analysis revealed that the model possesses substantial predictive power, explaining 74.4% of the variance in Technology Engagement (R^2=0.744) and 66.4% in Student Engagement (R^2=0.664). AI Personalization & Adaptivity emerged as the most dominant predictor, significantly influencing Technology Engagement (β =0.516, p < 0.001) and Student Engagement directly (β=0.310, p =0.010). Conversely, AI Usage and Feedback showed no significant direct effects on Student Engagement but demonstrated significant positive indirect effects through Full Mediation of Technology Engagement99. Conclusion: The findings confirm that Technology Engagement acts as a critical "gatekeeper" mechanism. The intensity of AI usage and automatic feedback alone is insufficient to drive academic engagement unless students first establish a strong sense of control and psychological engagement with the technology. Thus, educational strategies should prioritize adaptive personalization over mere instrumental use.]]>
