<![CDATA[The rapid adoption of artificial intelligence (AI) in education has created new opportunities for personalized learning, adaptive feedback, and academic support. However, existing findings remain fragmented, particularly regarding the mechanisms through which AI contributes to students’ academic achievement. This study systematically synthesizes recent literature on AI-based learning and identifies the technological, psychological, behavioral, and contextual factors that influence academic achievement. A systematic literature review was conducted following the PRISMA 2020 guidelines. Literature was retrieved from the Scopus database on 8 June 2026. From 780 initial records, 59 studies met the eligibility criteria and were included in the final qualitative synthesis. Thematic analysis identified recurring patterns and conceptual relationships across the selected studies. Five main themes were identified: AI literacy and technological readiness, psychological factors, student engagement, self-regulated learning, and contextual and institutional factors. This review interprets self-regulated learning as a key explanatory mechanism in the proposed conceptual framework, based on thematic evidence related to feedback use, goal setting, monitoring, reflection, engagement, motivation, and strategic learning, rather than as an empirically tested mediator across most included studies. Key challenges included academic dishonesty, overdependence on AI, misinformation, cognitive overload, and ethical concerns. AI does not automatically improve academic achievement; rather, its contribution depends on how it supports students’ motivation, engagement, and self-regulated learning within an enabling institutional context. The study proposes an integrated conceptual framework showing that AI literacy and technological readiness strengthen psychological factors, which enhance student engagement, promote self-regulated learning, and ultimately contribute to academic achievement. Keywords: Academic achievement, Self-regulated learning, Student engagement, AI literacy, Generative AI]]>
