<![CDATA[Background: Mental health problems and declining learning engagement among university students have become major concerns within digitally intensive higher education environments. Although artificial intelligence (AI) technologies have increasingly been integrated into educational systems, previous studies have rarely combined personalized mental health education, adaptive coping recommendations, and behavioral engagement analytics within a single intervention framework. Therefore, this study aimed to examine the effectiveness of AI. personalized mental health education in improving stress coping behavior and learning engagement among university students.Method: This study employed a sequential explanatory mixed methods intervention design involving 289 undergraduate students from Universitas Hang Tuah Pekanbaru and Institut Kesehatan Payung Negeri Pekanbaru, Indonesia, during 2025. Data were collected using structured questionnaires and behavioral engagement analytics from digital learning activities. Quantitative data were analyzed using paired sample t tests and SEM PLS, while qualitative data were analyzed thematically.Result: The findings demonstrated statistically significant improvements across all psychosocial and academic engagement dimensions (p<0.001). It induced positive changes in students stress coping behavior and learning engagement following the intervention. SEM PLS analysis additionally revealed significant direct and indirect effects of AI personalized mental health education on stress coping behavior, emotional wellbeing, and learning engagement. Qualitative findings further indicated that students perceived the intervention as adaptive, emotionally supportive, and beneficial for improving self regulation and academic motivation. These findings provide preliminary evidence that AI personalized mental health education may contribute to improved stress coping behavior and learning engagement among university students.]]>
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