AI in Education: A Systematic Literature Review of Emerging Trends, Benefits, and Challenges

John Jr. G. Adil

Seminars in Medical Writing and Education, 2025, vol. 4, 795

Abstract: Introduction: artificial intelligence (AI) is reshaping education by enabling personalized learning, improving instructional practices, and automating academic and administrative tasks. Despite its accelerating adoption, evidence on AI’s effectiveness, challenges, and broader implications remains fragmented across technologies, contexts, and outcomes. Method: this study conducted a systematic literature review of peer-reviewed publications from January 2020 to August 2024, following PRISMA 2020 guidelines. Searches across Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, ERIC, and the first 100 Google Scholar results were screened, appraised, and synthesized thematically. Thirty-nine studies meeting the inclusion criteria were analyzed. Results: the synthesis revealed emerging trends in AI applications spanning special education, K–12 schooling, higher education, vocational training, and language learning. Reported benefits included personalized learning pathways, improved pedagogy and assessment, enhanced feedback mechanisms, reduced administrative workload, and increasing emphasis on AI literacy for both educators and students. Persistent challenges involved infrastructural limitations, inadequate teacher training, algorithmic bias, ethical and data-privacy concerns, and inequities in access. Notable research gaps included a shortage of classroom-based empirical evidence, limited ethical frameworks, underrepresentation of marginalized populations, and insufficient strategies for AI literacy development. Conclusions: AI holds transformative potential to enrich teaching, learning, and educational equity. Realizing this promise requires targeted investments in infrastructure and teacher professional development, integration of AI literacy into curricula, and the establishment of robust ethical and governance frameworks. Expanding empirical research—particularly in underrepresented contexts—will be critical to ensuring AI’s responsible and inclusive integration into education.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:medicw:v:4:y:2025:i::p:795:id:795

DOI: 10.56294/mw2025795

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