Leveraging Artificial Intelligence (AI) as a tool for Burnout Detection in Educational Workers in Kazakhstan: An Natural Language Processing (NLP) Approach

Abstract

Burnout among workers in educational institutions has become a significant concern, particularly in high-pressure environments such as Kazakhstan’s public education sector. Emotional exhaustion, driven by increased workloads and administrative demands, has led to declining employee well-being and productivity. This study explores the use of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), to detect burnout by analyzing communication patterns among workers. Data were collected from five major educational institutions, including internal communications, feedback forms, and transcribed meetings. The AI model employed sentiment analysis and keyword tracking to identify linguistic indicators of stress, emotional exhaustion, and burnout.

The results demonstrate that AI-based burnout detection systems are highly effective, achieving an accuracy of 85% in predicting burnout in high-stress departments such as Academic Affairs and Student Services. When compared with self-reported burnout data, the AI system flagged a 75% burnout risk, higher than the 60% reported by employees, indicating that AI can detect latent burnout.

The study concludes that AI-driven tools can provide valuable insights for managing employee burnout, offering a proactive solution for educational institutions.Further research is needed, also tosupport the potential of AI as a critical tool in preventing burnout and promoting long-term employee wellness.