Document Type : Original Article

Authors

1 Assistant Professor, Department of Educational Sciences, Farhangian University, Tehran, Iran

2 Professor, Department of Educational Sciences, University of Tabriz

Abstract

Purpose: The rapid integration of artificial intelligence (AI) technologies in higher education offers significant opportunities but also introduces technostress among university faculty. This study qualitatively explores faculty members’ lived experiences of technostress in the context of AI-based learning environments.
Methodology: This qualitative study employed an interpretive phenomenological approach. Twenty-one faculty members from the University of Tabriz and Farhangian University of Tabriz were purposefully selected. Data were collected through semi-structured interviews and analyzed using Colaizzi’s seven-step method. The trustworthiness of the findings was ensured through the criteria of credibility, transferability, dependability, and confirmability.
Findlings: The data analysis revealed four main themes: (1) antecedents of technostress (individual–psychological factors, educational–organizational conditions, the nature of technology, and environmental–social norms); (2) consequences and impacts (weakened teaching–research performance, threats to professional quality of life, and psychological challenges); (3) professional growth and adaptation of faculty in the face of technostress (teaching innovation, development of digital skills, enhanced research opportunities, and increased flexibility); and (4) confronting strategies (individual strategies, organizational support, and policy and planning measures).
Conclusion: Despite differing views on the positive or negative nature of technostress, this phenomenon can influence faculty members’ mental health, professional quality of life, and academic productivity. At the same time, beyond its negative effects, it may also provide opportunities for professional growth, instructional innovation, and the development of digital skills.
Value: This study contributes to a deeper understanding of the interplay between emerging technologies, organizational environments, and faculty well-being, offering practical strategies to empower faculty members and inform the design of more supportive digital learning environments.

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Main Subjects

Aktan, O., & Toraman, Ç. (2022). The relationship between technostress levels and job satisfaction of teachers within the COVID-19 period. Education and Information Technologies, 27, 10429–10453. https://doi.org/10.1007/s10639-022-11027-2
Belarmino, J. P., Resma, J., Senial, C. R., & Sarsale, J. (2025). Depression, anxiety, and stress: Prevalence and narratives among Filipino pre-service teachers. Revista de Estudios e Investigación en Psicología y Educación, 12(1), Article e11168. https://doi.org/10.17979/reipe.2025.12.1.11168
Califf, C. B. (2022). Stressing affordances: Towards an appraisal theory of technostress through a case study of hospital nurses’ use of electronic medical record systems. Information and Organization, 32(4), Article 100431. https://doi.org/10.1016/j.infoandorg.2022.100431
Camacho, S., & Barrios, A. (2022). Teleworking and technostress: Early consequences of a COVID-19 lockdown. Cognition, Technology & Work, 24(3), 441–457. https://doi.org/10.1007/s10111-022-00693-4
Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: The sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 44(9), 7991–8000. https://doi.org/10.1007/s12144-025-07433-7
Chan, C. K. Y., & Tsi, L. H. Y. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83, Article 101395. https://doi.org/10.1016/j.stueduc.2024.101395
Chuang, Y.-T., Chiang, H.-L., & Lin, A.-P. (2025). Insights from the job demands–resources model: AI’s dual impact on employees’ work and life well-being. International Journal of Information Management, 83, Article 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887
Consiglio, C., Massa, N., Sommovigo, V., & Fusco, L. (2023). Techno-stress creators, burnout and psychological health among remote workers during the pandemic: The moderating role of e-work self-efficacy. International Journal of Environmental Research and Public Health, 20(22), Article 7051. https://doi.org/10.3390/ijerph20227051
Dragano, N., & Lunau, T. (2020). Technostress at work and mental health: Concepts and research results. Current Opinion in Psychiatry, 33(4), 407–413. https://doi.org/10.1097/YCO.0000000000000613
Engelbauer, M. (2023). Global assessment of recent UNESCO Biosphere Reserve quality enhancement strategies and interlinkages with other UNESCO labels. Würzburg University Press. https://doi.org/10.25972/WUP-978-3-95826-197-6
Erten, P., & Özdemir, O. (2020). The digital burnout scale development study. Inonu University Journal of the Faculty of Education, 21(2), 668–683. https://doi.org/10.17679/inuefd.597890
Estrada-Muñoz, C., Castillo, D., Vega-Muñoz, A., & Boada-Grau, J. (2020). Teacher technostress in the Chilean school system. International Journal of Environmental Research and Public Health, 17(15), Article 5280. https://doi.org/10.3390/ijerph17155280
Estrada-Muñoz, C., Vega-Muñoz, A., Castillo, D., Müller-Pérez, S., & Boada-Grau, J. (2021). Technostress of Chilean teachers in the context of the COVID-19 pandemic and teleworking. International Journal of Environmental Research and Public Health, 18(10), Article 5458. https://doi.org/10.3390/ijerph18105458
Gabbiadini, A., Paganin, G., & Simbula, S. (2023). Teaching after the pandemic: The role of technostress and organizational support on intentions to adopt remote teaching technologies. Acta Psychologica, 236, Article 103936. https://doi.org/10.1016/j.actpsy.2023.103936
Jain, K. K., & Raghuram, J. N. V. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies, 29(13), 17169–17209. https://doi.org/10.1007/s10639-024-12506-4
James, R., Sahu, M., & Gulfiza. (2023). Technostress and its impact on mental wellbeing and academic productivity of Gen Z and millennials. International Journal of Advanced Multidisciplinary Research, 10(1), 26–39. https://doi.org/10.22192/ijamr.2023.10.01.003
Lee, J. C., & Xiong, L. N. (2022). Investigation of the relationships among educational application (APP) quality, computer anxiety and student engagement. Online Information Review, 46(1), 182–203. https://doi.org/10.1108/OIR-08-2020-0348
Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, Article 101410. https://doi.org/10.1016/j.techsoc.2020.101410
Lopez, A., & Garza, R. (2023). Consumer bias against evaluations received by artificial intelligence: The mediation effect of lack of transparency anxiety. Journal of Research in Interactive Marketing, 17(6), 831–847. https://doi.org/10.1108/JRIM-07-2021-0192
Morrow, R., Rodriguez, A., & King, N. (2015). Colaizzi’s descriptive phenomenological method. The Psychologist, 28(8), 643–644. https://eprints.hud.ac.uk/id/eprint/26984
Nascimento, L., Correia, M. F., & Califf, C. B. (2024). Towards a bright side of technostress in higher education teachers: Identifying several antecedents and outcomes of techno-eustress. Technology in Society, 76, Article 102428. https://doi.org/10.1016/j.techsoc.2023.102428
Özgür, H. (2020). Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling. Computers in Human Behavior, 112, Article 106468. https://doi.org/10.1016/j.chb.2020.106468
Panisoara, I. O., Lazar, I., Panisoara, G., Chirca, R., & Ursu, A. S. (2020). Motivation and continuance intention towards online instruction among teachers during the COVID-19 pandemic: The mediating effect of burnout and technostress. International Journal of Environmental Research and Public Health, 17(21), Article 8002. https://doi.org/10.3390/ijerph17218002
Pasupuleti, R. S., & Thiyyagura, D. (2024). An empirical evidence on the continuance and recommendation intention of ChatGPT among higher education students in India: An extended technology continuance theory. Education and Information Technologies, 29(14), 17965–17985. https://doi.org/10.1007/s10639-024-12573-7
Saleem, F., & Malik, M. I. (2023). Technostress, quality of work life, and job performance: A moderated mediation model. Behavioral Sciences, 13(12), Article 1014. https://doi.org/10.3390/bs13121014
Seo, J.-H., & Bae, H.-O. (2024). Job stress and burnout affecting the mental health of Korean medical faculty members: Constructing causality among latent variables. Korean Journal of Medical Education, 36(1), 27–39. https://doi.org/10.3946/kjme.2024.282
Suyo-Vega, J. A., Meneses-La-Riva, M. E., Fernández-Bedoya, V. H., Alarcón-Martínez, M., Ocupa-Cabrera, H. G., Alvarado-Suyo, S. A., Polonia, A. D. C., & Miotto, A. I. (2022). Educational policies in response to the pandemic caused by the COVID-19 virus in Latin America: An integrative documentary review. Frontiers in Education, 7, Article 918220. https://doi.org/10.3389/feduc.2022.918220
Tarafdar, M., Maier, C., Laumer, S., & Weitzel, T. (2020). Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior. Information Systems Journal, 30(1), 96–124. https://doi.org/10.1111/isj.12253
Tiwari, V. (2021). Countering effects of technostress on productivity: Moderating role of proactive personality. Benchmarking: An International Journal, 28(2), 636–651. https://doi.org/10.1108/BIJ-06-2020-0313
Vallone, F., Galvin, J., Cattaneo Della Volta, M. F., Akhtar, A., Chua, S., Ghio, E., Giovazolias, T., Kazakou, Z., Kritikou, M., Koutra, K., Kovacevic, S., Lee-Treweek, G., Mašková, I., Mavritsaki, E., Nastic, J., Plassova, M., Stuchlíková, I., & Zurlo, M. C. (2023). Technostress and academic motivation: Direct and indirect effects on university students’ psychological health. Frontiers in Psychology, 14, Article 1211134. https://doi.org/10.3389/fpsyg.2023.1211134
Yang, H.-J., Cheng, Y., Liu, Y.-L., & Cheng, W.-J. (2025). Techno-strain and techno-insecurity are associated with poor mental well-being in specific age and occupation groups. Journal of Occupational Health, 67(1), Article uiae079. https://doi.org/10.1093/joccuh/uiae079