A Systematic Literature Review of Artificial Intelligence (AI) and Academic Performance in Higher Education

Abstract

Artificial intelligence (AI) is increasingly embedded in higher education, with growing interest in its effects on students’ academic performance. Despite its rapid adoption, evidence regarding its benefits, risks, and implementation conditions remains limited. This systematic literature review synthesizes recent empirical evidence on the impact of AI on academic performance in higher education, and identifies the key opportunities and challenges associated with AI integration. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Scopus database was conducted for English language open-access journal articles published between 2023 and 2025. Using predefined inclusion criteria, 3,344 records were screened, resulting in 20 eligible studies for qualitative synthesis. Data were analyzed thematically across methodologies, contexts, and outcomes. The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy. Predictive models have achieved high accuracy in identifying at-risk students and supporting early intervention. However, challenges, including AI dependency, academic integrity concerns, data privacy risks, and unequal benefits across student groups, have consistently been reported. AI has substantial potential to enhance academic performance in higher education, but its effectiveness depends on thoughtful pedagogical integration, institutional readiness, and ethical governance. Balanced, inclusive, and well-regulated AI adoption is essential for maximizing benefits while mitigating risks.