Predictive AI Framework for Real-Time Stress Monitoring and Personalized Interventions in Smart Societies
Bibliographic record
Abstract
Both forms of stress, distress and eustress, undeniably affect students' academic performance, mental health and well-being. This paper introduced an artificial intelligence (AI) powered Student Stress Detection and Recommendation System that integrated machine learning classification, personalized recommendations and visual analytics for facilitating early interventions. The system would aggregate self-reported stress indicators, behaviors, and academic habits modelled from stress indicators for categorizing distress into Distress (High) and Eustress (Low) levels through supervised learning models and also produce personalized recommendations based on the type of stress e.g., breathing exercises, meditation, discussions with mentor, activities to progressively achieve skill development goals. The framework has included a bar graph showing how accurate the model's predictions were and a distribution plot illustrating the focus of stress recommendations to provide better interpretability and to track stress focus. The system was developed as a full end-to-end architecture bringing the model to life and engaged a full data preprocessing, model training, accuracy evaluation, recommendation mapping, and visualizations. The framework proposed here could be assimilated into an academic decision-support framework to manage student well-being, productivity, and cognitive load in a proactive manner. The proposed approach provides a novel integration of predictive analytics and personalized support options to improve stress management for educational environments for scalable support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".