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Generative AI in Higher Education: A Systematic Review of Its Effects on Learning Outcomes and Academic Performance

Kellie Hon

2025Journal of Educational Technology Systems7 citationsDOIOpen Access PDF

Abstract

The integration of Generative AI tools in higher education has sparked significant interest, yet their impact on student learning engagement and academic performance remains unclear. This SLR examines empirical studies to determine whether Generative AI enhances engagement and improves learning outcomes in higher education. Following the PRISMA framework, we analyzed peer-reviewed articles from 2018 to 2024, identifying key trends, methodologies, and conflicting findings. Results reveal mixed evidence: while some studies report increased engagement and performance, others highlight limitations, including over-reliance on AI and variable effectiveness across disciplines. The review also identifies gaps in longitudinal and large-scale studies, calling for more rigorous research to assess Generative AI's pedagogical impact. Findings provide insights for educators and policymakers on optimizing Generative AI integration while addressing potential challenges.

Topics & Concepts

Generative grammarGenerative modelPsychologyKey (lock)Empirical researchComputer scienceAcademic achievementHigher educationSystematic reviewExperiential learningStudent engagementMathematics educationEmpirical evidenceArtificial intelligenceEducational researchVariable (mathematics)Component (thermodynamics)Artificial Intelligence in Healthcare and EducationOnline Learning and AnalyticsExplainable Artificial Intelligence (XAI)
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