Questionnaire “Students' attitudes towards the use of artificial intelligence technologies in educational activities”: development and psychometric characteristics

Context and relevance. Artificial intelligence (AI) is becoming an integral part of the educational environment, influencing the forms of educational activity, assessment methods, and interaction between students and teachers. Despite the recognition of its effectiveness, ambivalence in students' attitudes is noted: along with trust and technological optimism, anxiety and concerns about the decrease in autonomy and fairness of the application of algorithms are recorded. To design educational systems and assess the digital readiness of students, a tool is needed to validly measure students' attitudes towards the use of AI in education. Objective. To develop and psychometrically substantiate a questionnaire designed to diagnose students' attitudes towards the use of artificial intelligence technologies in educational activities. Hypothesis. Students' attitudes towards AI are a two-factor structure, including positive perception (trust, efficiency, personalization) and apprehension (anxiety, mistrust, fears of loss of subjectivity). Methods and materials. The study included four stages: conceptual and analytical, expert assessment of content validity (N = 11 experts), survey research (N = 503 students of various levels of education, aged 17–32, 54,3% women), and retest verification (N = 21). The methods of exploratory and confirmatory factor analysis, Cronbach’s alpha coefficients, correlation analysis with external scales (TTQ), and assessment of retest reliability were used. Results. The questionnaire includes 20 items distributed across two scales: “Positive Attitude toward AI” and “Absence of Apprehension toward AI”. Notably, all items forming the second scale are reverse-coded in the final version. After reverse transformation, higher scores reflect reduced apprehension and a more positive attitude toward AI, whereas lower scores indicate greater apprehension. Interpretation requires taking this scoring direction into account. Both factors demonstrated high rates of internal consistency (α = 0,84 and α = 0,87). Exploratory and confirmatory factor analyses confirmed the two-factor model. The theoretically expected correlations were found: a positive attitude is associated with technophilia (r = 0,58), and an absence of apprehension associated with technophobia (r = 0,52). The retest procedure showed the stability of the scales over time (r = 0,71 and r = 0,68, respectively). Conclusions. The developed questionnaire is a valid and reliable tool for diagnosing students' attitudes towards AI in educational activities. It allows recording the ambivalence of students' attitudes, a combination of technological optimism and apprehension. The tool can be used in applied research on the digital transformation of education, monitoring students' readiness to interact with intelligent systems, as well as in comparative and longitudinal studies.

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