Teacher Motivation in AI-Integrated School Environments
DOI:
https://doi.org/10.54536/ajet.v5i1.6407Keywords:
Artificial Intelligence, Motivation, Teacher Professional Development, Teacher TrainingAbstract
This study investigates how Herzberg’s Two-Factor Theory and Dinham and Scott’s three-domain model of teacher satisfaction apply in the context of AI integration in secondary education. Using qualitative data from secondary school teachers, the research identifies how motivators, such as professional growth, recognition, and pedagogical innovation, are influenced by hygiene factors and systemic conditions. Contrary to Herzberg’s assumption of independence between motivators and hygiene factors, findings reveal that in AI-driven educational environments, intrinsic motivators are fragile and contingent on stable extrinsic and systemic support, including reliable infrastructure, technical assistance, and clear AI-related policies. The absence of such support fosters anxiety, resistance, and burnout, particularly in policy-volatile contexts. The study also highlights the roles of leadership, collegiality, and professional community in mediating AI adoption, aligning with Dinham and Scott’s emphasis on systemic forces in sustaining teacher satisfaction. Policy implications include equitable investment in digital infrastructure, development of context-specific AI guidelines, and institutional recognition of innovative AI pedagogy. Practically, embedding AI literacy in workload-sensitive professional development and fostering peer-support networks are essential to sustaining motivation and reducing anxiety. By reframing motivator–hygiene dynamics through a contingency lens, this research extends Herzberg’s model to technology-driven education and underscores the foundational role of systemic conditions in enabling motivators to function effectively. These findings contribute to theory refinement and offer actionable insights for policymakers, school leaders, and practitioners seeking to balance technological innovation with teacher well-being.
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References
Aghayeva, J. (2019). Changing the way we teach in college: Faculty motivation in Azerbaijan [Doctoral dissertation, Michigan State University].
Ajikere, N. A. (2024). Herzberg two factor theory as a correlate of teachers’ motivation in public senior secondary schools in Rivers State. International Journal of Economics, Environmental Development and Society, 5(4), 469-482.
Akçaba, P., Toklu, D. A., & Akcil, U. (2024). Examining the attitudes and anxiety of teachers and administrators towards artificial intelligence: Relational browsing. Brain Broad Research in Artificial Intelligence and Neuroscience, 15(4), 325-327. https://doi.org/10.70594/brain/15.4/22
Akdemir, E. (2020). The determination of teachers’ motivation based on Herzberg’s Motivation Theory. Turkish Online Journal of Educational Technology, 19(4), 89–101.
Alasgarova, R., & Rzayev, J. (2024). The changing role of educators in the age of artificial intelligence: Molding minds at the digital dawn. Ubiquity Proceedings, 6. https://doi.org/10.5334/uproc.128
Al-Zahrani, A. M. (2024). Unveiling the shadows: Beyond the hype of AI in Education. Heliyon, 10(9). https://doi.org/10.1016/j.heliyon.2024.e30696
American Psychological Association 7th Edition. (2019). Ethical principles of psychologists and code of conduct. American Psychological Association. https://www.apa.org/ethics/code
Ataliç, H., Can, A., & Cantürk, N. (2016). Herzberg’s motivation- hygiene theory applied to high school teachers in Turkey. European Journal of Multidisciplinary Studies, 1(4), 90. https://doi.org/10.26417/ejms.v1i4.p90-97
Bakhadirov, M., Alasgarova, R., & Rzayev, J. (2024). Factors influencing teachers’ use of artificial intelligence for instructional purposes. IAFOR Journal of Education, 12(2), 9–32. https://doi.org/10.22492/ije.12.2.01
Cao, M. (2024). Teacher-student relationships in the context of Artificial Intelligence. Advances in Educational Technology and Psychology, 8(4), 75–80. https://doi.org/10.23977/aetp.2024.080412
Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative and mixed methods approaches (6th ed.). SAGE.
Dan, Z., & Subramaniam, G. (2023). Using Two-Factor theory to examine teacher turnover intention – a case of a private university in Sichuan, China. Journal of Humanities and Social Sciences Research, 5(2), 37–47. https://doi.org/10.37534/bp.jhssr.2023.v5.n2.id1220.p37
Diganayeva, A. (2021). Teacher well-being at schools in Azerbaijan [Master’s thesis. Eötvös Loránd University].
Dinham, S., & Scott, C. (2000). Moving into the third, outer domain of teacher satisfaction. Journal of Educational Administration, 38(4), 379–396. https://doi.org/10.1108/09578230010373633
Fuenzalida, Á. J. M. (2021). A Preliminary measure of Teachers’ Occupational Well-Being in Baku Schools and contextual factors affecting it. Khazar Journal of Humanities and Social Sciences, 24(1), 37–65. https://doi.org/10.5782/2223-2621.2021.24.1.37
Furnham, A., & Cuppello, S. (2025). Personality, intrinsic and extrinsic work motivation, and satisfaction. Psychology, 16(01), 26–38. https://doi.org/10.4236/psych.2025.161003
Galeng, R. R. (2025). Assessing the Impact of Teacher Competency and Engagement on Job Satisfaction through the Lens of Teacher-Student Interaction. American Journal of Education and Technology, 4(2), 93–101. https://doi.org/10.54536/ajet.v4i2.4335
Hazzan-Bishara, A., Kol, O., & Levy, S. (2025). The factors affecting teachers’ adoption of AI Technologies: A unified model of external and internal determinants. Education and Information Technologies, 30(11), 15043–15069. https://doi.org/10.1007/s10639-025-13393-z
Herzberg, F., Mausner, B., & Snydermann B. (1959). The motivation to work. New York: Wiley.
Judge, B., Nitzberg, M., & Russell, S. (2024). When code isn’t law: Rethinking regulation for artificial intelligence. Policy and Society, 44(1), 85–97. https://doi.org/10.1093/polsoc/puae020
Lee, B., Lee, C., Choi, I., & Kim, J. (2022). Analyzing determinants of job satisfaction based on Two-Factor Theory. Sustainability, 14(19), 12557. https://doi.org/10.3390/su141912557
Malabanan, E. J. R. (2025). Leading the Digital School: Exploring the Relationship between Learning Management System Practices and School Leadership in Senior High Schools. American Journal of Education and Technology, 4(3), 71–78. https://doi.org/10.54536/ajet.v4i3.5409
Martínez-Comesaña, M., Rigueira-Díaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J., Ocarranza-Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review. Revista De Psicodidáctica (English Ed ), 28(2), 93–103. https://doi.org/10.1016/j.psicoe.2023.06.002
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396. https://doi.org/10.1037/h0054346
Miah, M. T., & Hasan, M. J. (2022). Impact of Herzberg Two-Factor Theory on Teachers’ Job satisfaction: An Implication to the Private Universities of Bangladesh. International Journal of Business and Management Research, 10(1), 1–5. https://doi.org/10.37391/ijbmr.100101
Mikayilova, U., & Kazimzade, E. (2016). Teachers as Reflective Learners: Teacher perception of professional development in the context of Azerbaijan’s curriculum reform. Voprosy Obrazovaniya/ Educational Studies Moscow, 2, 125–145. https://doi.org/10.17323/1814-9545-2016-2-125-145
Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x.
Nasirov, R. (2024). Azərbaycanda müəlimlərin peşə məmnuniyyəti [Teachers’ job satisfaction in Azeraijan]. Azərbaycan məktəbi, 1(706), 41-48. 10.30546/32898065.2024.1.48
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8). https://doi.org/10.29333/ejmste/13428
Patton, M. Q. (2015). Qualitative Research & Evaluation Methods: Integrating Theory and Practice. Sage.
Rojas, M., Méndez, A., & Watkins-Fassler, K. (2023). The hierarchy of needs empirical examination of Maslow’s theory and lessons for development. World Development, 165, 106185. https://doi.org/10.1016/j.worlddev.2023.106185
Sindhvad, S., Mikayilova, U., & Kazimzade, E. (2020). Factors influencing instructional leadership capacity in Baku, Azerbaijan. Educational Management Administration & Leadership, 50(1), 81–98. https://doi.org/10.1177/1741143220938364
World Medical Association. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization, 79(4), 373 - 374. World Health Organization. https://iris.who.int/handle/10665/268312
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