Artificial Intelligence, Digital Literacy, and Self-Regulated Learning as Determinants of Academic Performance: A Systematic Literature Review of Cognitive and Organizational Mechanisms

• Saefulah Saefulah

  Author

• Zidan Ahmad

  Author

• Jihaad Ahmad

  Author

The rapid integration of artificial intelligence (AI), particularly generative artificial intelligence (GenAI), has transformed contemporary learning systems and raised new questions about how educational institutions should manage technology-enabled academic performance. Although recent studies report positive effects of AI-supported learning on academic achievement, the evidence remains conditional, suggesting that performance improvement depends not only on access to AI tools but also on students’ AI literacy, digital literacy, self-regulated learning, cognitive load, and learning-environment support. This study aims to synthesize recent empirical and review-based evidence on the mechanisms through which AI-related factors influence students’ academic performance and to propose an integrated performance-oriented framework for educational organizations. A systematic literature review was conducted on peer-reviewed studies published between 2021 and 2026, focusing on AI in education, GenAI, AI literacy, digital literacy, self-regulated learning, cognitive load, learning environment, and academic achievement. The synthesis identifies several interrelated findings. First, AI-supported learning can improve academic achievement when AI functions as instructional and cognitive support rather than as a substitute for students’ reasoning. Second, AI literacy and digital literacy shape students’ capacity to evaluate, interpret, verify, and use AI-generated information appropriately. Third, self-regulated learning serves as a key mechanism linking AI use with academic performance because students must plan goals, monitor understanding, regulate strategies, and verify AI outputs during learning. Fourth, cognitive load operates as a critical boundary condition because AI may reduce unnecessary mental effort but may also increase cognitive burden when information is excessive, unreliable, or poorly structured. The review also identifies important research gaps, particularly the limited integration of AI literacy, digital literacy, self-regulated learning, cognitive load, and learning-environment support within a unified educational performance model, as well as the need for stronger empirical evidence in secondary-school contexts. Based on these findings, the review proposes an integrated framework in which AI literacy, AI-supported learning, digital literacy, self-regulated learning, cognitive load, and learning-environment support interact to influence academic achievement. The findings suggest that educational institutions should manage AI adoption through measurable learning-performance indicators rather than technology-adoption metrics alone.

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