Artificial intelligence-supported learning and higher-order cognitive outcomes: the mediating role of metacognitive self-regulation

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Abstract
The growing adoption of AI in higher learning institutions poses important questions regarding its effects on higher-order cognitive competencies of students. Although previous studies have investigated perceptions of learning influenced by AI, very little information exists regarding the cognitive processes involved in AI interactions influencing learning performance. The present study sought to establish the relationship between AI task scaffolding, AI verification literacy, and cognitive offloading tendency in terms of their impacts on critical thinking and technical problem-solving of learners, with metacognitive self-regulation as the mediating factor. A cross-sectional design was employed using data from 533 university students across Ghana. Structural equation modeling (SEM) with bias-corrected bootstrapping in AMOS (version 23) was used to estimate the hypothesized relationships. The results indicate that AI task scaffolding is positively associated with both critical thinking and technical problem-solving, whereas cognitive offloading tendency is negatively associated with these outcomes. AI verification literacy does not directly predict the dependent variables but exerts indirect effects through metacognitive self-regulation. These results suggest that the educational value associated with AI-supported learning depends on how students engage with AI systems. The study contributes to the literature by shifting attention from AI adoption to AI interaction mechanisms and highlighting metacognitive self-regulation as the key pathway linking AI-supported learning to higher-order cognitive outcomes.

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