Prolonged computer use can change the workload and strain a person experiences, but these changes are hard to see in task output alone. This paper presents CognitoShield AI, a privacy-oriented platform that estimates stress-related behavioral signals and cognitive workload indicators from interaction timing. The implemented pipeline aggregates telemetry into 30-second observations, derives a seven-feature representation, and applies an XGBoost regression model. A personal-baseline signal and a bounded context-aware fusion stage add personalization, and a FastAPI backend with a web interface presents the estimates together with recovery-oriented guidance. On a synthetic held-out test set, the model reached a mean absolute error (MAE) of 10.29, a root mean square error of 13.15, and an R-squared value of 0.7916. In an exploratory check of nine real users, the production seven-feature model recorded an MAE of 35.2367, while the P7 and P13 variants recorded 35.6300 and 35.2644, respectively. These results are preliminary and cannot establish generalization or clinical validity. The design does not collect raw typed characters and uses no camera-based or facial analysis. CognitoShield AI is therefore offered as a behavioral estimation and self-awareness tool, not a diagnostic system.

Balaraj Mallappa Paranatti, Bharath G M, C V Puneeth Raj, Dhanush Y S, Prof. Kamaleswari P,"CognitoShield AI: An Intelligent Platform for Digital Burnout and Cognitive Load Detection" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(931-936) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.