LAURA: An Awareness-Oriented Explainable AI Framework for Academic Stress in Higher Education

Academic stress represents a significant challenge in higher education, especially for students navigating unfamiliar academic environments. In such contexts, difficulties in understanding expectations, feedback, and performance standards can affect engagement, participation, academic performance, and decision-making. Despite significant advances in learning analytics and artificial intelligence, current educational systems remain limited in their ability to support students’ sense-making of their academic conditions. Existing systems predominantly focus on prediction and performance monitoring, offering limited support for interpretation and awareness, which are essential for understanding academic stress, particularly in complex or unfamiliar learning environments. To address this limitation, this study introduces the LAURA framework (Learning Context Modeling, Academic Stress Assessment, Understanding-Oriented Explanation, Reflection Support, and Adaptive Awareness Feedback), an awareness-oriented framework designed to support students in understanding their academic stress through interaction. Unlike traditional AI approaches that prioritize prediction, LAURA conceptualizes academic stress as an interpretive process and positions explainable AI as a mechanism to support students’ awareness. The framework integrates transparent modeling, explanation, and structured reflection within a unified interaction cycle, enabling students to interpret academic signals, examine contributing factors, and develop a more informed understanding of their conditions. Grounded in appraisal-based stress theory and principles of human-centered AI, LAURA extends existing approaches by embedding explanation and reflection as core components of system design rather than auxiliary features. In this way, it shifts the role of explainable AI from revealing model behavior to supporting meaning-making processes within educational contexts. The proposed framework contributes a new perspective on AI-supported educational systems by emphasizing awareness as the primary outcome. It provides a conceptual and design foundation for developing student-facing systems that not only analyze academic conditions but also support students in making sense of them, particularly in diverse and complex educational environments. As a conceptual framework, LAURA provides a foundation for future implementation and empirical validation in educational settings.

@article{Almathami2026,
  title     = {LAURA: An Awareness-Oriented Explainable AI Framework for Academic Stress in Higher Education},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ahmed Almathami and Richard Stone},
  doi       = {10.14569/IJACSA.2026.0170770},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170770}
}