AI-enhanced teaching quality, skill development, and student satisfaction in higher education: Implications for SDG 4 from Kazakhstan

Rajasekhara Mouly Potluri, Mariyam Taskinbayeva, Assel Kenesovna Jumasseitova, Yerzhan B. Mukashev

World Development Sustainability, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1016/j.wds.2026.100372
Data confidence 85 high
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Abstract

This study examines business students’ perceptions of teaching quality, skill development, and overall satisfaction in artificial intelligence (AI)-enhanced higher education, aligned with Sustainable Development Goal 4 (SDG 4), which emphasizes inclusive, effective, and future-ready education. The study emphasizes key instructional and outcome variables without repetition of title-based terminology to improve clarity and readability. Using a cross-sectional quantitative design, survey data were collected from 861 undergraduate and postgraduate business students in higher education institutions in Kazakhstan, exceeding recommended sample size thresholds for structural equation modeling (SEM). Measurement reliability and validity were confirmed using Cronbach’s alpha and the Rasch Rating Scale Model. The SEM demonstrated acceptable model fit (CFI>0.90; RMSEA<0.08) and was used to test relationships among usability, accessibility, personalization, engagement, feedback, AI-supported competency development, and educational outcomes. Findings indicate that usability, accessibility, personalization, and student engagement significantly predict perceived teaching quality in AI-enhanced learning environments, while content quality shows no significant independent effect. Additionally, AI-supported development of technical, problem-solving, and interpersonal skills explains substantial variance in student satisfaction, reinforcing SDG 4 objectives related to competency-based and inclusive education. Variations in tuition fee structures and study modes highlight persistent disparities in student satisfaction outcomes. Although the cross-sectional and self-reported design limits causal inference and broader generalizability, the study provides robust empirical evidence from an underrepresented Central Asian context. It offers practical implications for designing equitable, skill-oriented, and AI-supported pedagogical systems in higher education. Overall, the study highlights AI’s role in strengthening inclusive, competency-driven, and sustainable business education aligned with SDG 4.