Abstract:

This research investigates the integration of machine learning (ML), wearable physiological biomarkers, and natural language processing (NLP) to develop a scalable framework for real-time stress assessment and management in educational settings. Student stress, a critical barrier to academic performance and mental well-being, is often inaccurately captured through traditional self-reported methods. To address this gap, the study combines continuous physiological monitoring via wearable devices (Empatica EmbracePlus) with NLP-driven analysis of student-written reflections, aiming to establish an objective, multimodal approach to stress prediction and intervention. Data was collected from graduate students balancing academic and professional responsibilities over multiple weeks, capturing physiological indicators (e.g., heart rate variability, skin conductance, temperature) alongside weekly reflective prompts. Supervised ML models, including random forests and deep learning architectures, were trained to classify stress levels (low, moderate, high) by fusing physiological signals with linguistic features extracted from reflections using large language models (LLMs). The NLP component identified stress-related biometrics such as negative sentiment, cognitive load indicators (e.g., terms like “overwhelmed” or “pressure”), and thematic patterns in unstructured text. Preliminary results demonstrate that multimodal integration of wearable and textual data enhances stress prediction accuracy compared to single-modality models, with physiological and linguistic features exhibiting consistent correlations. Cross-validation and alignment with self-reported stress logs validated the framework’s reliability, though challenges emerged in interpreting complex deep learning outputs and addressing ethical concerns around continuous biometric data collection. The study underscores the potential of AI-driven tools to enable proactive, personalized interventions, such as real-time feedback and resource recommendations, while emphasizing the need for transparent, privacy-preserving systems in educational contexts. By bridging research and practice, this work advances the operationalization of wearable technology and NLP in academia, offering a blueprint for scalable mental health support. This work provides a practical foundation for scalable mental health support in academic environments. Future work will focus on adaptive intervention design, explainable artificial intelligence methods, and ethical guidelines for multimodal data use.

Keywords:

Stress prediction, machine learning, educational technology, wearable biomarkers, natural language processing, mental health interventions.