<![CDATA[Academic stress has become an important concern among university students because of its potential impact on physical health, psychological well-being, and academic performance. Conventional stress-assessment approaches are often limited by subjective evaluation and the lack of continuous monitoring capabilities. This study aimed to design SynBioSense, a conceptual framework and prototype for student stress monitoring that integrates Internet of Things and Artificial Intelligence technologies. A Design Research approach was employed to develop the system architecture, monitoring workflow, conceptual verification framework, and mobile application prototype. The proposed framework was designed to integrate multimodal physiological sensing, cloud-based infrastructure, Artificial Intelligence-assisted analysis, and mobile-based visualization within a unified digital-health architecture. The study resulted in conceptual system architecture, workflow model, and user-interface prototype that illustrated how monitoring, visualization, recommendation, and user-support functionalities could be integrated into a student-oriented platform. The study contributed an integrated conceptual design that may serve as a foundation for future implementation, validation, and deployment of digital stress-monitoring systems in higher-education environments. Future research should focus on system implementation, Artificial Intelligence model development, and empirical evaluation involving university students]]>
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