The Impact of Educational Artificial Intelligence Technologies on Teachers' Job Satisfaction and Mental Health
DOI:
https://doi.org/10.63053/ijhes.185Keywords:
Teachers, Educational Artificial Intelligence, Mental Health, Job Satisfaction, Technology in Education..Abstract
Job characteristics and the expansion of modern educational technologies are among the factors affecting teachers' mental health and job satisfaction. The main aim of this study was to investigate the role of using educational artificial intelligence technologies on mental health and job satisfaction among teachers. The research method was quantitative, applied in terms of purpose, and semi-experimental with a pretest-posttest design including a control group. The statistical population consisted of secondary school teachers in Kashan, Iran. A total of 40 teachers were selected using convenience sampling and randomly assigned into two groups of experimental and control (20 participants each). Data were collected using the General Health Questionnaire (GHQ-28) and the Minnesota Satisfaction Questionnaire (MSQ). The reliability of the questionnaires was confirmed by calculating Cronbach's alpha coefficient. The results of data analysis indicated that the use of educational artificial intelligence technologies led to significant improvement in mental health and increased job satisfaction in the experimental group compared to the control group. In conclusion, the findings suggest that the application of artificial intelligence in educational planning and teacher support can be effective in promoting mental health and increasing job satisfaction among teachers.
References
1- Ortega-Auris, Á. S., Padilla Caballero, J. E. A., & Ortega Auris, J. S. (2025). Work overload and artificial intelligence in teachers: A systematic review. Revista Impulso, 5(10), 227-242.
2- Duan, H., & Zhao, W. (2024). The effects of educational artificial intelligence-powered applications on teachers' perceived autonomy, professional development for online teaching, and digital burnout. International Review of Research in Open and Distributed Learning, 25(3), 57-76.
3- Liu, G., Lan, G., Nneli, N. C., Song, F., Xiao, Q., & Zheng, M. (2025). Balancing generative AI integration and faculty well-being: evidence from Chinese higher education. Frontiers in Education.
4- Gyasi, P. A., & Sun, B. (2026). The impact of digital technology on teacher resilience, potential benefits and challenges: a review. Cogent Education, 13(1), 2642498.
5- Liu, F., et al. (2026). Threat or opportunity? Challenge vs hindrance appraisals of generative AI and self-regulated teaching among pre-service EFL teachers: A COR perspective. Acta Psychologica, 265, 106747.
6- Neyazi, A., Rahimi, B. A., Sifat, S., Razaqi, N., Amirzada, E., Afzali, H., Neyazi, M., & Mohammadi, A. Q. (2024). Psychometric evaluation of the Dari version of the 28-item General Health Questionnaire (GHQ-28) in Afghanistan. BMC Psychology.
7- Malakouti, S. K., Fatollahi, P., Mirabzadeh, A., & Zandi, T. (2007). Reliability, validity and factor structure of the GHQ-28 used among elderly Iranians. International Psychogeriatrics, 19(4), 623-634.
8- Mitzner, T. L., Savla, J., Boot, W. R., Sharit, J., Charness, N., Czaja, S. J., & Rogers, W. A. (2019). Technology adoption by older adults: Findings from the PRISM trial. The Gerontologist, 59(1), 34-44.
9- Mohsen, A., Ghafari, S., Sayedi, R., & Ivascu, L. (2024). Keys to happiness: Exploring the determinants of job satisfaction in private schools of Afghanistan. Journal of Business and Economic Analysis.
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