PUW! Cognitive Strength in Context: An Innovative, Multi-Modal Framework for Real-Time Analysis of Stress and Socio-Emotional Functioning

Background: Student well-being is a multidimensional construct encompassing physical and mental health. However, its assessment in educational contexts remains fragmented, failing to comprehensively understand the diverse factors that shape students’ experiences. This gap is particularly pronounced for cognitively strong students (CSS), whose unique intellectual, emotional, and social challenges are frequently overlooked by traditional approaches. Objective: Current work introduces a methodology - aligned with ethical scientific expectations - to develop a holistic understanding of student well-being by integrating multimodal data. The approach will uncover key patterns that influence well-being and academic performance, offering actionable insights to guide targeted interventions in educational settings. Methods: The framework, developed through an extensive literature review and refined using the Delphi method, integrates three core components: physiological measures (e.g., heart rate variability and sleep data) to detect stress; self-reporting tools for qualitative insights; and survey instruments to capture psychological stressors and socio-economic influences. While broadly applicable, it emphasizes CSS-specific stressors. Results: We collect qualitative and quantitative data based on self-reports and physiological measures. Large language models are leveraged for sentiment analyses of qualitative data. Classic machine learning techniques analyze physiological data and integrate quantitative data from multiple modalities. This approach aims to identify patterns in stress and coping mechanisms, offering insights into broader dimensions of well-being. Conclusion: This research advances AI-driven educational tools, with an ethically grounded, data-driven approach to support student development, particularly for CSS.