Psychological Science and Education
2026. Vol. 31, no. 3, 35–49
doi:10.17759/pse.2026310303
ISSN: 1814-2052 / 2311-7273 (online)
Artificial intelligence use motives: adaptation of the diagnostic tool
Abstract
Context and relevance. Artificial intelligence is a technology with the potential to fundamentally transform all spheres of human life. Its rapid integration into everyday reality intensifies research dedicated to the psychology of using neural networks. However, the development of empirical research in the Russian scientific field is limited by the lack of validated psychodiagnostic tools that allow assessing users' attitudes toward neural networks and the specific features of their motivation for using them. Objective. To adapt a questionnaire for diagnosing the motives of using artificial intelligence (AI) for the Russian population and to validate it. Hypothesis. It was assumed that the two-factor structure of the “Artificial Intelligence Use Motives” questionnaire would make it possible to create a Russian-language version of the tool with satisfactory psychometric properties, and that the composition of its factors would replicate the original model. Methods and materials. The study involved 368 university and secondary vocational education students (mean age 19 years old, 75% of the sample were women). Convergent validity was tested using the Adolescent and Parent Technology Use Attitude Questionnaire and the Career Engagement Scale. For data processing and analysis, exploratory and confirmatory factor analyses and Spearman correlations were used. Results. The two-factor structure of the “Artificial Intelligence Use Motives” questionnaire was confirmed as “Expectancy related to AI use” and “Subjective task value”. The second factor contains four subscales. All scales are characterized by high internal consistency, and the overall psychometric indicators of the questionnaire confirm its reliability and validity. Conclusions. The Russian-language version of the AI use motives questionnaire retains the factor structure of the original version and possesses sufficient psychometric properties for its use in psychological science and practice.
General Information
Keywords: artificial intelligence (AI), artificial intelligence use motives, attitudes towards technology, psychodiagnostics, expectancy, subjective task value
Journal rubric: Interdisciplinary Researches
Article type: scientific article
DOI: https://doi.org/10.17759/pse.2026310303
Funding. The study was implemented in the framework of the Basic Research Program at HSE University (HSE-BR-2025-077).
Supplemental data. Dataset is available: https://doi.org/10.48612/MSUPE/v73m-ba1n-5at1.
Received 07.02.2026
Revised 02.04.2026
Accepted
Published
For citation: Volkova, N.V., Kochetkov, N.V., Chiker, V.A. (2026). Artificial intelligence use motives: adaptation of the diagnostic tool. Psychological Science and Education, 31(3), 35–49. (In Russ.). https://doi.org/10.17759/pse.2026310303
© Volkova N.V., Kochetkov N.V., Chiker V.A., 2026
License: CC BY-NC 4.0
References
- Волкова, Н.В., Бордунос, А.К., Чикер, В.А. (2026). Адаптация методики диагностики развития карьеры: шкала «Карьерная вовлеченность». Социальная психология и общество, 17(1), 146–165. https://doi.org/10.17759/sps.2026170109
Volkova, N.V., Bordunos, A.K., Chiker, V.A. (2026). Adaptation of the questionnaire for career development: “Career engagement scale”. Social Psychology and Society, 17(1), 146—165. (In Russ.). https://doi.org/10.17759/sps.2026170109 - Иванюшина, В.А., Александров, Д.А., Мусабиров, И.Л. (2016). Структура академической мотивации: Ожидания и субъективные ценности освоения университетского курса. Вопросы образования, 4, 229—250. https://doi.org/10.17323/1814-9545-2016-4-229-250
Ivanyushina, V.A., Aleksandrov, D.A., Musabirov, I.L. (2016). The structure of academic motivation: Expectations and subjective values of mastering a university course. Educational Issues, 4, 229—250. (In Russ.). https://doi.org/10.17323/1814-9545-2016-4-229-250 - Казакова, Е.И., Кузьминов, Я.И. (2025). Мы должны воспитать культуру критического отношения к ответам искусственного интеллекта. Вопросы образования, 1, 8—24. https://doi.org/10.17323/vo-2025-25882
Kazakova, E.I., Kuz'minov, Ya.I. (2025). We must cultivate a culture of critical thinking about artificial intelligence responses. Educational Issues, 1, 8—24. (In Russ.). https://doi.org/10.17323/vo-2025-25882 - Кузьменко, М.В. (2025). Искусственный интеллект в школьном математическом образовании: осведомленность, готовность и использование учителями математики. Психологическая наука и образование, 30(3), 125–139. https://doi.org/10.17759/pse.2025300310
Kuzmenko, M.V. (2025). Artificial intelligence in school mathematics education: awareness, readiness, and usage among mathematics teachers. Psychological Science and Education, 30(3), 125–139. https://doi.org/10.17759/pse.2025300310 - Мещерякова, Н.Н. (2012). Проявления аномии в российском обществе. Мир науки, культуры, образования, 3(34), 281—283.
Meshcheryakova, N.N. (2012). Manifestations of Anomie in Russian Society. World of Science, Culture, Education, 3(34), 281—283. (In Russ.). - Солдатова, Г.У., Нестик, Т.А., Рассказова, Е.И., Дорохов, Е.А. (2021). Психодиагностика технофобии и технофилии: разработка и апробация опросника отношения к технологиям для подростков и родителей. Социальная психология и общество, 12(4), 170—188. (In Russ.). https://doi.org/10.17759/sps.2021120410
Soldatova, G.U., Nestik, T.A., Rasskazova, E.I., Dorokhov, E.A. (2021). Psychodiagnostics of Technophobia and Technophilia: Development and Testing a Questionnaire of Attitudes towards Technology for Adolescents and Parents. Social Psychology and Society, 12(4), 170—188. (In Russ.). https://doi.org/10.17759/sps.2021120410 - Субботина, М.В. (2024). Искусственный интеллект и высшее образование — враги или союзники. Вестник Российского университета дружбы народов, Серия: Социология. 24(1), 176—183. https://doi.org/10.22363/2313-2272-2024-24-1-176-183
Subbotina, M.V. (2024). Artificial intelligence and higher education: enemies or allies? Bulletin of the Peoples' Friendship University of Russia, Series: Sociology, 24(1), 176—183. (In Russ.). https://doi.org/10.22363/2313-2272-2024-24-1-176-183 - Bellemare-Pepin,, Lespinasse, F., Thölke, P. et al. (2026). Divergent creativity in humans and large language models. Scientific Report, 16(1279). https://doi.org/10.1038/s41598-025-25157-3
- Brislin, R.W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
- Cheung, G.W., Cooper-Thomas, H.D., Lau, R.S., Wang, L.C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
- Eccles, J.S., Wigfield, A. (2002). Motivational Beliefs, Values, and Goals. Annual Review of Psychology, 53, 109— https://doi.org/10.1146/annurev.psych.53.100901.135153
- Gocen, A., Aydemir, F. (2020). Artificial Intelligence in Education and Schools. Research on Education and Media, 12(1). https://doi.org/10.2478/rem-2020-0003
- Hirschi, A., Freund, P.A., Herrmann, A. (2014). The Career Engagement Scale: Development and Validation of a Measure of Proactive Career Behaviors. Journal of Career Assessment, 22(4), 575— https://doi.org/10.1177/1069072713514813
- Lauermann, F., Tsai, Y.-M., Eccles, J.S. (2017). Math-related career aspirations and choices within Eccles et al.’s expectancy–value theory of achievement-related behaviors. Developmental Psychology, 53(8), 1540— https://doi.org/10.1037/dev0000367
- McGrath, M.J., Lack, O., Tisch, J., Duenser, A. (2025). Measuring trust in artificial intelligence: validation of an established scale and its short form. Frontiers in Artificial Intelligence. 8:1582880. https://doi.org/10.3389/frai.2025.1582880
- Mayer, J.D. (2025). How Human Personality Will Change With the Use of Artificial Intelligence. Personality Science, 6. https://doi.org/10.1177/27000710251386963
- Perkins, M., Furze, L., Roe, J., MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/53761/q3azde36
- Rönkkö, M., Cho, E. (2022). An Updated Guideline for Assessing Discriminant Validity. Organizational Research Methods, 25(1), 6–14. https://doi.org/10.1177/1094428120968614
- Schepman, A., Rodway, P. (2022). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction, 39(8), 1—18. https://doi.org/10.1080/10447318.2022.2085400
- Wang, Y.-Y., Wang, Y.-S. (2019). Development and validation of anartificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(2), 1—16. https://doi.org/10.1080/10494820.2019.1674887
- Wigfield, A., Eccles, J.S. (2000). Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1), 68— https://doi.org/10.1006/ceps.1999.1015
- Yu, H., Guo, Y. (2023). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Frontiers in Education, 8(1183162). https://doi.org/3389/feduc.2023.1183162
- Yuan, L., Liu, X. (2025). The effect of artificial intelligence tools on EFL learners’ engagement, enjoyment, and motivation. Computers in Human Behavior, 162(108474). https://doi.org/10.1016/j.chb.2024.108474
- Yurt, E., Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A Contribution to Investigating the Connection between AI and Motivation. International Journal of Technology in Education, 7(2), 308—325. https://doi.org/10.46328/ijte.725
Information About the Authors
Contribution of the authors
Natalia V. Volkova — data collection and analysis; application of statistical and mathematical methods for data analysis; writing and design of the manuscript.
Nikita V. Kochetkov — annotation, literature review; visualization of research results.
Vera A. Chiker — ideas; planning of the research; control over the research.
All authors participated in the discussion of the results and approved the final text of the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Ethics statement
Informed consent was obtained from all study participants.
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