Artificial intelligence–enabled teaching: Insights from Kazakhstan higher education students
Potluri R.M. Gabdullin M. Jumasseitova A.K. Mukashev Y.B.
June 2026Elsevier Ltd
Social Sciences and Humanities Open
2026#13
The accelerated adoption of artificial intelligence (AI) in higher education has intensified expectations regarding instructional quality and learning effectiveness, yet empirical evidence on its pedagogical value remains contextually contingent. This study investigates students perceptions of AI-enabled teaching in Kazakhstan, with a specific focus on perceived instructional quality dimensions and discipline-related skill development. Grounded in contemporary AI-supported learning literature, a structured self-administered questionnaire was developed and administered using a stratified random sampling design across higher education institutions. Instrument reliability and construct validity were established through Cronbachs alpha and the Rasch Rating Scale Model using a pilot sample representing 10% of respondents. Power analysis indicated a minimum requirement of 1504 observations; 2700 valid responses were ultimately analyzed. Data were processed using Microsoft Excel and RStudio, and partial least squares structural equation modeling (PLS-SEM) was employed to test seven hypothesized relationships within the proposed framework. The measurement model exhibited satisfactory reliability and validity, while the structural model showed low multicollinearity, strong explanatory and predictive power, and acceptable fit indices (SRMR, NFI, GoF). The findings reveal that usability, engagement, content quality, and accessibility of AI-based instruction significantly enhance student satisfaction, while instructional quality strongly predicts perceived skill acquisition, particularly in problem-solving, conceptual understanding, and technological competence. Conversely, perceived gains in interpersonal and diagnostic skills were comparatively weaker, and feedback-related pathways were not statistically significant, indicating limitations in current AI feedback mechanisms. The study offers robust perception-driven empirical evidence on the pedagogical implications of AI-integrated instruction in Kazakhstans higher education system and provides actionable insights for evidence-based instructional design and AI-enabled teaching policy.
AI-Enabled teaching , Artificial intelligence in education , Higher education in Kazakhstan , Power calculation , Skills development , Student perceptions , Technology-enhanced learning
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Kazakh-British Technical University, 59, Tole-bi Street, Almaty, 050000, Kazakhstan
Kazakh-British Technical University
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