Adaptation of a UTAUT/TAM-based questionnaire to study factors of AI use by Russian university educators

Context and relevance. The active development of artificial intelligence (AI) technologies and critical debates about its potential and risks are leading to a rethinking of the role of teachers in higher education. Research on how teachers perceive and use AI is considered important, but there is a lack of such studies and validated measurement tools in the Russian context. Objective. To adapt and validate a foreign questionnaire for studying the factors influencing the use of AI technologies by teachers at Russian universities. Methods and materials. The study involved 103 teachers from 26 Russian universities. For validation, we used confirmatory factor analysis (CFA) to test a 6-factor structure (“Awareness”, “Risks”, “Difficulties”, “Conditions”, “Attitude”, “Implementation”), assessed reliability (Cronbach’s α and McDonald’s ω), and conducted regression analysis to identify predictors. Results. Confirmatory factor analysis (CFA) confirmed a stable 6-factor structure for the tool with high reliability (Cronbach's α = 0,85-0,86) and validity scores (CFI = 0,932; RMSEA = 0,068), which meet international standards for psychometric testing. Regression analysis identified key determinants of AI use by university teachers: facilitating conditions (β = 0,39; p < 0,001), teachers' attitudes (β = 0,29; p < 0,01), and perceived risks (β = –0,29; p < 0,001). Conclusions. Evidence of validity and reliability is provided for the adapted questionnaire as a tool for diagnosing the factors of AI use in Russian higher education. The results emphasize that for integrating AI into teaching practice, it is critically important not only to inform teachers but, first and foremost, to create favorable organizational conditions and foster a positive attitude, while simultaneously reducing the perception of risks.

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