AI as Double-Edged Sword: A Quantitative study of Teacher Efficiency, Cognitive Load and Burnout in Education

Authors

  • Nazia Fardous Govt. Graduate College for Women, Haji Pura, Sialkot, Pakistan
  • Muhammad Idrees Punjab Higher Education Department, Pakistan
  • Muhammad Tanveer Afzal Department of Science education, Allama Iqbal Open University, Islamabad, Pakistan

DOI:

https://doi.org/10.54938/ijemdss.2026.05.5.863

Keywords:

AI, Efficiency, Cognitive load, Burnout, Secondary education

Abstract

Teaching practices  have been transformed by Artificial Intelligence (AI) integration   in education. Use of AI has automated administrative tasks. Instructional delivery has been enhanced, and personalized learning is supported by using AI in education. Its use  promises to increase efficiency and reduce workload, but there is emerging evidence that AI usage may contribute to burnout among teachers simultaneously by increasing stress caused by technology usage and cognitive load . This study explores dual role of AI-based educational technologies towards burnout, by analyzing whether AI works as a tool of relief or a source of  psychological stress by using quantitative research design with a sample of 500  school teachers. In this study the relation between use of AI, perceived efficiency, cognitive load, and levels of burnout has been analyzed. The findings of this study indicate that while AI  usage in education significantly improve task efficiency (r = 0.62), it also pose new problems  by elevating mental workload that contribute to burnout (R² = 0.49) among teachers. It is concluded, based on the finding of this study that AI usage in education works as a double-edged sword by providing both benefits and problems. It is emphasized that there a dire need for  strategies to implement AI usage in education with a proper balance reducing problems while enhancing its benefits.

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References

Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8

Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Ingersoll, R. M. (2001). Teacher turnover and teacher shortages: An organizational analysis. American Educational Research Journal, 38(3), 499–534.

Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20, 56. https://doi.org/10.1186/s41239-023-00426-1

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behavior, 2(2), 99–113.

Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28, 8445–8501. https://doi.org/10.1007/s10639-021-10728-w

OECD. (2021). AI and the future of education. Organisation for Economic Co-operation and Development.

Ouyang, F., Zheng, L., Jiao, P., & Moore, M. G. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2023.100118

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Skaalvik, E. M., & Skaalvik, S. (2017). Teacher stress and teacher self-efficacy as predictors of engagement, emotional exhaustion, and motivation to leave the teaching profession. Creative Education, 8(12), 1785–1799.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2015). Crossing to the dark side: Examining creators, outcomes, and inhibitors of technostress. MIS Quarterly, 39(4), 831–858.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2021). AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025

Zhang, L., & Liu, X. (2022). Digital transformation and teacher workload in smart classrooms. Computers & Education, 182, 104463.

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Published

2026-10-02

How to Cite

Nazia Fardous, Muhammad Idrees, & Muhammad Tanveer Afzal. (2026). AI as Double-Edged Sword: A Quantitative study of Teacher Efficiency, Cognitive Load and Burnout in Education. International Journal of Emerging Multidisciplinaries: Social Science, 5(5), 670–689. https://doi.org/10.54938/ijemdss.2026.05.5.863

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Section

Research Article