Artificial Intelligence and Machine Learning Approaches for Monitoring and Managing Teacher Stress in Higher Education Institutions

Teacher stress has emerged as a critical challenge in higher education, with far-reaching implications for both educators and institutions. This chapter explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) technologies in monitoring, managing, and alleviating stress among teachers in academic environments. Through a detailed examination of AI-based solutions, such as predictive models, wearable technologies, and emotion recognition systems, this chapter provides insights into how data-driven approaches can enhance teacher well-being and improve teaching effectiveness. Key applications of AI, including personalized stress management strategies, real-time feedback systems, and workload optimization, are discussed in the context of their impact on stress reduction and productivity enhancement. Ethical considerations, such as data privacy, algorithmic bias, and informed consent, are also addressed, highlighting the challenges that must be navigated to ensure these technologies are used responsibly and effectively. With increasing pressure on educators to perform at high levels, AI-driven interventions represent a promising path forward to create more supportive, sustainable teaching environments. The chapter emphasizes the transformative potential of AI and ML in reshaping how institutions address teacher stress, fostering both teacher well-being and improved educational outcomes.