Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{207736,
        author = {SRIPRIYA N D},
        title = {AI-Based Detection of Emotional Burnout from Typing Patterns},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {2468-2470},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207736},
        abstract = {Burnout has become a common concern among students, teachers, and working professionals. Continuous workload and stress can affect concentration, motivation, productivity, and overall well-being. Burnout is usually identified through questionnaires, interviews, and self-reported assessments. Although these methods are useful, they are generally conducted at specific intervals and rely on individuals describing their experiences. This study proposes a method of examining typing behaviour as a possible additional indicator of burnout-related changes. The proposed approach considers various typing characteristics, including typing speed, inter-key time, key-hold duration, pauses, typing errors, backspace usage, and changes in typing rhythm. Three machine-learning techniques, namely Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), are considered for analysing these patterns. The proposed system is intended to support early awareness and research on behavioural changes and is not intended to diagnose burnout.},
        keywords = {Artificial Intelligence, Emotional Burnout, Keystroke Dynamics, Typing Behaviour, Machine Learning, Random Forest, SVM, LSTM.},
        month = {August},
        }