Generative AI use, self-regulation, and Neuro-AI Pedagogical competence in higher education: a comparative study of undergraduate and graduate students in Uzbekistan
Abstract
This study examines how full-time undergraduate and graduate students at a leading technical university in Uzbekistan engage with generative AI tools and how this relates to cognitive regulation, motivation, self-regulation, and ethical awareness. Using a quantitative exploratory design, an online survey was administered to 381 participants during the first (autumn) semester of the 2025–2026 academic year. A preliminarily screened 30-item instrument was developed across seven scales AI Use, Digital Attention, Cognitive Load, Motivation, Self-Regulation, Neuro-AI Pedagogical Competence, and Ethics and Academic Autonomy with internal consistency ranging from α = 0.717 to α = 0.895; content and discriminant validity have not yet been confirmed on independent samples. Group comparisons were conducted using Mann–Whitney U with Cohen's d on a balanced subsample ( n = 302; 151 per group), constructed to avoid confounding group-size asymmetry with substantive group differences. Significant between-group differences were found on six of seven scales ( p < .001): Ethics and Academic Autonomy (d = 1.47), AI Use (d = 1.29), Self-Regulation (d = 1.14), Neuro-AI Pedagogical Competence (d = 1.14), and Digital Attention (d = 0.99) showed large effects; Motivation showed a medium effect (d = 0.70). Cognitive load did not differ significantly ( p = .296). Multiple regression ( n = 372) identified Ethics and Academic Autonomy ( β = 0.446), Self-Regulation ( β = 0.279), and Motivation ( β = 0.178) as the strongest predictors of Neuro-AI Pedagogical Competence (R 2 = 0.799). The findings suggest that higher scores on Neuro-AI Pedagogical Competence were associated with stronger ethical awareness, self-regulation, and motivation, rather than with frequency of AI use. The study contributes empirical evidence from Central Asian higher education and supports the design of level-differentiated AI-integrated curricula.