The Emotional cost of Automation Exploring Teacher Anxiety and Role Identity in AI- Augmented Classrooms
DOI:
https://doi.org/10.47067/ramss.v8i2.538Keywords:
Teacher Anxiety, Role Identity, AI in Education, Classroom Automation, Teacher well-being, Digital Readiness, Professional Development, Educational Technology, AI Adoption, Punjab PakistanAbstract
The current study examined the impact of AI integration on the classes into the expression of teacher anxiety and the role identity of in-service teachers in the state of Pakistan (Punjab). It used a quantitative and cross sectional correlation design according to which the sample examined a stratified randomisation of 384 researchers who worked in the government and the private schools and colleges representing the secondary and higher secondary sectionsThe sample was also gender wide, qualification and experienced wise with most of the teachers being at mid-age of the career and held masters. The results showed there were critical relations between teacher anxiety-role identity (r= -0.42) and AI-integration (r= -0.35) and the role- identity-AI integration (r= 0.47). The perception of AI as the tool of facilitation served as a significant predictor of whether a person would adopt it ( 0.48, p < 0.001) and explained the variance to 23 percent using regression analysis. Chi-square results indicated that the higher the rate of AI integration, the higher the anxiety levels especially among the experienced teachers. The interviewing process pointed to the absence of anxiety as not being qualified but having problem coping with the new technologies without relevant institutional support. The findings are in concordance with Technology Acceptance Model in the sense that perceived ease of use along with perceived usefulness are factors that highlight a very important role.
References
Bhattacharyya, S. S. (2024). Co-working with robotic and automation technologies: technology anxiety of frontline workers in organisations. Journal of Science and Technology Policy Management, 15(5), 926-947.
Burke, P. J. (2004). Extending identity control theory: Insights from classifier systems. Sociological theory, 22(4), 574-594.
Chen, Z., Feng, X., & Zhang, S. (2022). Emotion detection and face recognition of drivers in autonomous vehicles in IoT platform. Image and Vision Computing, 128, 104569.
Day, C., Sammons, P., Hopkins, D., Leithwood, K., & Kington, A. (2008). Research into the impact of school leadership on pupil outcomes: Policy and research contexts. School Leadership and Management, 28(1), 5-25.
Farkaš, I. (2024). Transforming Cognition and Human Society in the Digital Age. Biological Theory, 1-13.
Hernandez-Avalos, I., Mota-Rojas, D., Mora-Medina, P., Martínez-Burnes, J., Casas Alvarado, A., Verduzco-Mendoza, A., Lezama-García, K., & Olmos-Hernandez, A. (2019). Review of different methods used for clinical recognition and assessment of pain in dogs and cats. International journal of veterinary science and medicine, 7(1), 43-54.
Holmes, W., & Littlejohn, A. (2024). Artificial intelligence for professional learning. In Handbook of artificial intelligence at work (pp. 191-211). Edward Elgar Publishing.
Joo, J., Gomez-Beldarrain, G., Kim, E., & Verma, H. (2024). Transition towards automatic Passenger Boarding Bridge: Themes of task delegation for autonomous airport operations.
Kelchtermans, S., & Santos, A. M. (2025). The study of policy coordination: an approach to integrate expert assessment with automated content analysis. Science and Public Policy, 52(2), 222-235.
Kim, K., Kwon, K., Ottenbreit-Leftwich, A., Bae, H., & Glazewski, K. (2023). Exploring middle school students’ common naive conceptions of Artificial Intelligence concepts, and the evolution of these ideas. Education and Information Technologies, 28(8), 9827-9854.
Koning, J. (2023). Reducing loneliness in seniors using an automated calling system for activity invitation University of Twente].
Kroes, S. K., Janssen, M. P., Groenwold, R. H., & van Leeuwen, M. (2021). Evaluating privacy of individuals in medical data. Health Informatics Journal, 27(2), 1460458220983398.
Langer, M., König, C. J., & Busch, V. (2021). Changing the means of managerial work: effects of automated decision support systems on personnel selection tasks. Journal of business and psychology, 36(5), 751-769.
Levin, M. (2022). Technological approach to mind everywhere: an experimentally-grounded framework for understanding diverse bodies and minds. Frontiers in systems neuroscience, 16, 768201.
März, V., & Kelchtermans, G. (2013). Sense-making and structure in teachers’ reception of educational reform. A case study on statistics in the mathematics curriculum. Teaching and teacher education, 29, 13-24.
McClure, C. E., Epler, R. T., Schmitt, L., & Rangarajan, D. (2024). AI in sales: Laying the foundations for future research. Journal of Personal selling & sales ManageMent, 44(2), 108-127.
McClure, E., & Wald, B. (2022). Algorithmic microaggressions. Feminist Philosophy Quarterly, 8(3/4).
Nastjuk, I., Trang, S., Grummeck-Braamt, J.-V., Adam, M. T., & Tarafdar, M. (2024). Integrating and synthesising technostress research: a meta-analysis on technostress creators, outcomes, and IS usage contexts. European Journal of Information Systems, 33(3), 361-382.
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied psychology, 71(4), 1171-1204.
Pedder, D. J. (2016). Emotion regulation capacity in older adults: Effects on facial expression and memory Australian Catholic University].
Piattoeva, N., & Saari, A. (2022). Rubbing against data infrastructure (s): methodological explorations on working with (in) the impossibility of exteriority. Journal of Education Policy, 37(2), 165-185.
Raffaghelli, J. E., Ferrarelli, M., & Rodríguez, N. L. (2025). Slowness as Postdigital Positionality in the Era of Generative AI: A Conversation. Postdigital Science and Education, 1-26.
Skaalvik, E. M., & Skaalvik, S. (2018). Job demands and job resources as predictors of teacher motivation and well-being. Social psychology of education, 21(5), 1251-1275.
Smith, L. W., Rose, R. L., Zablah, A. R., McCullough, H., & Saljoughian, M. M. (2023). Examining post-purchase consumer responses to product automation. Journal of the Academy of Marketing Science, 51(3), 530-550.
Susskind, R., & Susskind, D. (2022). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Tarafdar, M., Page, X., & Marabelli, M. (2023). Algorithms as co?workers: Human algorithm role interactions in algorithmic work. Information Systems Journal, 33(2), 232-267.
Tarafdar, R. (2025). AI-Supported Emotional Conflict Resolution: Technical Approaches and Implementation Strategies. IJSAT-International Journal on Science and Technology, 16(1).
Tate, T., & Warschauer, M. (2022). Equity in online learning. Educational Psychologist, 57(3), 192-206.
Topal, I. H. (2025). Research on Voscreen in English language education: A systematic literature review and content analysis. The JALT CALL Journal, 21(2), 2184-2184.
Van der Heijden, H., Geldens, J. J., Beijaard, D., & Popeijus, H. L. (2015). Characteristics of teachers as change agents. Teachers and teaching, 21(6), 681-699.
Wang, C., Li, Y., Fu, W., & Jin, J. (2023). Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce. Journal of Retailing and Consumer Services, 73, 103325.
Xu, J., & Howard, A. (2022). Evaluating the impact of emotional apology on human-robot trust. 2022 31st IEEE international conference on robot and human interactive communication (ro-man),
Zhan, X., Sun, D., Wen, Y., Yang, Y., & Zhan, Y. (2022). Investigating students’ engagement in mobile technology-supported science learning through video-based classroom observation. Journal of Science Education and Technology, 31(4), 514-527.





