Artificial Intelligence in Education: Systematic Review of Personalised Learning, Automation, and Ethical Integration

Abstract

This systematic literature review examines the role of artificial intelligence in education across three critical dimensions: personalised learning systems, administrative automation, and ethical integration frameworks. The study aims to synthesize current research evidence and identify key trends, challenges, and opportunities in AI-enhanced educational environments. A systematic literature review was conducted following PRISMA guidelines, searching Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and ERIC databases. The review analyzed 148 peer-reviewed studies published between 2020-2025, employing thematic analysis and quality assessment procedures to synthesize findings across the three research domains. AI-enabled personalised learning systems demonstrate significant effectiveness with 62% improvement in student test results and 30% enhancement in overall performance. Administrative automation reduces workload by 40% while improving accuracy. However, ethical challenges including algorithmic bias, privacy concerns, and the need for human-centered design remain critical implementation barriers. Findings inform educational policymakers, institutional administrators, curriculum designers, and technology developers implementing AI systems. The research provides evidence-based guidance for K-12 schools, higher education institutions, corporate training programs, and educational technology companies developing AI-enhanced learning solutions. This review uniquely integrates personalised learning, automation, and ethical considerations within a comprehensive framework, providing the first systematic synthesis of post-2020 AI education research. The study advances knowledge by identifying convergent themes and establishing evidence-based recommendations for responsible AI implementation in educational contexts.