Skip to main navigation menu Skip to main content Skip to site footer

Vol. 13 No. 01 (2026)
International Multidisciplinary Journal for Research & Development

Articles

PSYCHOLOGICAL ADAPTATION AND MOTIVATIONAL FACTORS IN TEACHERS' USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Published 2026-01-31

  • Atajanov Ibratbek Qudrat ugli

Atajanov Ibratbek Qudrat ugli
Lecturer, Department of Pedagogy and Psychology Urgench State University named after Abu Rayhan Beruni. Urgench, Uzbekistan

Abstract

 This article analyzes the psychological adaptation and motivational factors that emerge when teachers in general education and higher educational institutions use artificial intelligence technologies. Specifically, it examines the interrelationships between attitudes towards technology, technological anxiety, intrinsic and extrinsic motivation, the desire for professional self-development, and innovative competencies. The study employed a mixed-method approach, combining questionnaires and semi-structured interviews; data collected from 180 teachers were processed using descriptive statistics and correlation analysis. Results indicate that perceived usefulness and ease of use of the technology, technological self-efficacy, and intrinsic motivation are strong predictors of teachers' positive attitudes and intention to use [1,3,6,17]. While technological anxiety remains at a moderate level, it is found that professional development programs focused on self-improvement can reduce this anxiety and significantly enhance innovative competencies [4,9,13,19]. The conclusions drawn provide practical recommendations for educational policy, pedagogical psychology, and the design of teacher training programs.

Keywords

artificial intelligence technologies; technological anxiety; internal and external motivation; professional self-development; innovative competencies; technology adoption; pedagogical psychology

PDF

References

  1. Şimşek, A. S., Tepetaş Cengiz, G. Ş., & Bal, M. (2025). Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI. Education and Information Technologies.
  2. Acosta-Enriquez, B. G., Villena Zapata, L. V., Huamaní Jordan, O., et al. (2025). Acceptance of Artificial Intelligence as a Teaching Strategy Among University Professors. Human Behavior and Emerging Technologies.
  3. 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.
  4. Duan, H., & Zhao, W. (2024). The Effects of Educational AI-Powered Applications on Teachers' Perceived Autonomy, Professional Development, and Digital Burnout. The International Review of Research in Open and Distributed Learning.
  5. Farooq, Y. (2025). Exploring Teachers' Psychological Readiness for Effective AI Integration in the Classroom. The Critical Review of Social Sciences Studies.
  6. Zhang, C., Schießl, J., Plößl, L., et al. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education.
  7. Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers' trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology.
  8. Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with AI-based chatbot. Interactive Learning Environments.
  9. Tütünis, B., Ünal, K., & Köksal, Ö. (2025). AI-Enhanced Professional Development: Effects on Secondary EFL Teachers' Digital Competencies and Affective Dimensions. Journal of Posthumanism.
  10. Mah, D.-K., & Groß, N. (2024). Artificial intelligence in higher education: Exploring faculty use, self-efficacy, profiles, and professional development needs. International Journal of Educational Technology in Higher Education.
  11. Kamsin, I. F. (2025). Teacher's Acceptance and Intention to Use Artificial Intelligence Technology in Teaching and Learning Based on the UTAUT Model. International Journal of Information and Education Technology.
  12. Ilhan, B. (2025). Motivation to Teach and AI Anxiety among TESOL Student-Teachers. LLT Journal: A Journal on Language and Language Teaching.
  13. Xiang, Y., Yang, C., Jin, Z., & Zhao, W. (2025). Factors influencing the adoption of generative artificial intelligence into classroom teaching by university teachers. PLOS ONE.
  14. Wang, K., Ruan, Q., Zhang, X., et al. (2024). Pre-Service Teachers' GenAI Anxiety, Technology Self-Efficacy, and TPACK. Behavioral Sciences.
  15. Yi, Z., & Xia, S. (2025). The Impact of Pre-Service Language Teachers' Basic Psychological Needs on Behavioural Intentions to Utilise AI. European Journal of Education.
  16. Chen, C., Hu, W., & Wei, X. (2024). From anxiety to action: The impact of AI anxiety and AI self-efficacy on motivated learning. Interactive Learning Environments.
  17. Lin, T., Zhang, J., & Xiong, B. (2025). Effects of Technology Perceptions, Teacher Beliefs, and AI Literacy on AI Technology Adoption. Sustainability.
  18. Wang, Y.-M., Wei, C.-L., Lin, H.-H., et al. (2022). What drives students' AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments.
  19. Filiz, O., Kaya, M. H., & Adiguzel, T. (2025). Teachers and AI: Factors influencing AI integration in K-12 education. Education and Information Technologies.
  20. Galindo-Domínguez, H., Delgado, N., Urruzola, M.-V., et al. (2025). Using Artificial Intelligence to Promote Adolescents' Learning Motivation. Journal of Computer Assisted Learning.

Downloads

Download data is not yet available.