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Mental Health Framework

Wellness Monitoring in Academic Context

EduPilot's mental health framework helps students maintain academic wellness through intelligent monitoring and supportive interventions.

Cognitive Load Monitoring

EduPilot tracks cognitive load by analyzing multiple factors: task density, deadline proximity, concurrent critical tasks, and historical completion patterns. This creates a comprehensive view of mental workload that goes beyond simple task counting.

Stress Index Algorithm

The stress index is calculated on a scale of 1-10 using a weighted formula that considers upcoming deadlines, task complexity, time of day, and completion velocity. The algorithm adapts to individual student patterns over time for personalized insights.

Wellness Nudges

When the stress index exceeds configurable thresholds, EduPilot sends gentle wellness nudges. These aren't alerts but supportive suggestions to take breaks, reprioritize tasks, or seek support. The timing and frequency are carefully calibrated to avoid adding to cognitive load.

Privacy and Ethics

All mental health data is stored securely and is only accessible to the individual student. The system is designed as a supportive tool, not a diagnostic instrument. Students can adjust sensitivity levels or opt out of wellness features entirely.

Stress Index Calculation

The stress index algorithm combines multiple factors to provide a holistic view of academic wellness:

Input Factors

  • Upcoming critical tasks (weight: 30%)
  • Task completion velocity (weight: 25%)
  • Deadline density (weight: 20%)
  • Time of day patterns (weight: 15%)
  • Historical stress patterns (weight: 10%)

Wellness Thresholds

  • Low (1-3): Optimal performance zone
  • Moderate (4-6): Normal academic stress
  • High (7-8): Consider breaks recommended
  • Critical (9-10): Immediate attention needed

Important: This framework is designed for wellness support, not medical diagnosis. Always seek professional help for mental health concerns.

EduPilot. Department of Information Systems, FUTA.

Final year project by Adepitan M. and Adetoye S.