The problem of artificial intelligence technology acceptance in the educational environment: Pedagogical resistance and implementation strategies

Cover Page

Cite item

Full Text

Abstract

The article analyzes the problems of AI technology acceptance in the educational environment. The study is based on the AIDUA adoption model and includes empirical data on digital acceptance/ resistance to AI adoption by the pedagogical community. The authors identify the socio-psychological and organizational roots of pedagogical digital resistance, offering recommendations for acceleration of AI adoption in teaching practices. The results of the empirical study allow to characterize the attitudes of digital resistance to the introduction of AI technologies. Primarily, these are related to the underestimation of social influence and expectations arising from the speed of technology diffusion, concerns about the potential of using AI technologies and the possible replacement of the educational staff due to the non-anthropomorphic nature of digital assistants, and fears of losing the emotional and personal component of education. The authors also investigate the relevant factors of restraint at different levels of the pedagogical community due to the inaccessibility of necessary resources, the lack of common approaches and protocols for the use of AI technologies, resistance on the part of the pedagogical community based on the preservation of traditions and values of classical education. The proposed strategies and organizational approaches are aimed at reducing resistance and creating a favorable environmental climate conducive to the successful introduction of new technologies in the educational process. The article highlights the importance of a comprehensive approach and integrated strategy for the effective use of the potential of artificial intelligence in education.

About the authors

Andrey Petrovich Glukhov

Tomsk State Pedagogical University

Email: GlukhovAP@tspu.edu.ru
Tomsk, Russian Federation

Elena Stanislavovna Sinogina

Tomsk State Pedagogical University

Email: sinogina2004@mail.ru
Tomsk, Russian Federation

Sofia Anatolyevna Lomovskaya

Tomsk State Pedagogical University

Email: xxx_sofi_xxx@mail.ru
Tomsk, Russian Federation

References

  1. Kabudi T., Pappas I., Olsen D. H. AI-enabled adaptive learning systems: A systematic mapping of the literature // Computers and Education: Artificial Intelligence. 2021. Vol. 2. 12 p. https://doi.org/10.1016/j.caeai.2021.100017
  2. Deursen A. J. van, Dijk J. A. van. The first-level digital divide shifts from inequalities in physical access to inequalities in material access // New Media & Society. 2019. Vol. 21, Is. 2. P. 354–375.
  3. Rogers E. M. Diffusion of innovations. 5th ed. New York. Free Press, 2003. 576 p.
  4. Davis F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology // MIS quarterly. 1989. Vol. 13, № 3. P. 319–340.
  5. Gursoy D., Chi O. H., Lu. L., Nunkoo R. Consumers acceptance of artificially intelligent (AI) device use in service delivery // International Journal of Information Management. 2019. Vol. 49, Is. 5. P. 157–169.
  6. Venkatesh V., Morris M. G., Davis G. B., Davis F. D. User acceptance of information technology: Toward a unified view // MIS quarterly. 2003. Vol. 27, № 3. P. 425–478.
  7. Kashive N., Powale L., Kashive K. Understanding user perception toward artificial intelligence (AI) enabled elearning // The International Journal of Information and Learning Technology. 2020. Vol. 38, № 1. P. 1–19.
  8. Lin C. Y., Xu N. Extended TAM model to explore the factors that affect intention to use AI robotic architects for architectural design // Technology Analysis & Strategic Management. 2022. Vol. 34, № 3. P. 349–362.
  9. Kaplan A., Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence // Business horizons. 2019. Vol. 62, № 1. P. 15–25.
  10. Zhou H., Huang M., Zhang T., Zhu X., Liu B. Emotional chatting machine: Emotional conversation generation with internal and external memory // Proceedings of The Thirty-Second AAAI Conference on Artificial Intelligence. 2018. Vol. 32, № 1. P. 730–738.
  11. Song X., Xu B., Zhao Z. Can people experience romantic love for artificial intelligence? An empirical study of intelligent assistants // Information & Management. 2022. Vol. 59, № 2. 10 p. doi: 10.1016/j.im.2022.103595
  12. Kelly S., Kaye Sh., Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics. 2023. Vol. 77. 33 p. doi: 10.1016/j.tele.2022.101925
  13. Behl A., Chavan M., Jain K., Sharma I., Pereira V. E., Zhang J. Z. The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations // International Journal of Manpower. 2022. Vol. 43, № 2. P. 569–586.
  14. Королева Д. О., Андреева А. А., Хавенсон Т. Е. Шоковая инновация: концептуализация процесса цифровой трансформации образования в период пандемии // Образование и саморазвитие. 2023. Т. 18, № 2. С. 100–117. doi: 10.26907/esd.18.2.08
  15. Королева Д. О., Науширванов Т. О. Digital countries. Особенности цифровизации образования в России, Венгрии и Германии // Образовательная политика. 2021. № 3 (87). С. 106–118. doi: 10.22394/2078-838X-2021-3-106-118
  16. Gansser O. A., Reich C. S. A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. 2021. Technology in Society. Vol. 65. 15 p. doi: 10.1016/j.techsoc.2021.101535
  17. Chatterjee S., Bhattacharjee K. K. Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling // Education and Information Technologies. 2020. Vol. 25. P. 3443–3463.
  18. Tran K., Nguyen T. Preliminary research on the social attitudes toward AI’s involvement in Christian education in Vietnam: promoting AI technology for religious education // Religions. Vol. 12, № 3. 20 p. doi: 10.3390/rel12030208
  19. Сидорова Т. А. Образы восприятия и концептуализация антропологических вызовов искусственного интеллекта // ΠΡΑΞΗMΑ. Проблемы визуальной семиотики (ΠΡΑΞΗMΑ. Journal of Visual Semiotics). 2024. Вып. 1 (39). С. 102–119. doi: 10.23951/2312-7899-2024-1-102-119
  20. Валеев А. С., Худайбердина С. Р., Валеева Г. Х. Оценка отношения педагогов организаций высшего и среднего профессионального образования к инновациям в управлении образованием // Мир науки. Педагогика и психология. 2023. Т. 11, № 2. С. 12.

Supplementary files

Supplementary Files
Action
1. JATS XML


Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).