Modeling AI-Enhanced Learning for Sustainable Cognitive Development: A UTAUT2 and Bloom’s Taxonomy Perspective

Background/purpose. This study examines the behavioral factors influencing the adoption of AI-powered generative technologies in higher education and their impact on students’ cognitive engagement—a crucial element of sustainable, inclusive, and high-quality learning, as envisioned by UNESCO’s Sustainable Development Goal 4 (SDG4). The study combines Bloom’s Taxonomy hierarchical structure with the expanded Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. Materials/methods. Structural equation modeling (SEM) was utilized to analyze data collected from a sample of 731 university students, aiming to identify the key determinants of GenAI adoption and to assess its subsequent effects across six distinct cognitive domains. Results. The SEM analysis reveals that performance expectancy, facilitating conditions, and hedonic motivation have a significant influence on AI adoption, underscoring the importance of usefulness, support, and enjoyment in promoting student engagement. The study demonstrates that the adoption of GenAI significantly predicts student engagement, which in turn acts as a critical mediator in enhancing mid-level cognitive performance by improving understanding, application and analysis abilities. In contrast, marginal impacts were found in evaluation and creation domains while remembering did not demonstrate any significant effect. Conclusion. The study provides evidence-based insights for educators, curriculum designers, and policymakers seeking to leverage GenAI as part of equitable, student-centered, and cognitively enriching AI-enabled learning environments.