INTEGRATING ARTIFICIAL INTELLIGENCE IN TEACHING AND LEARNING: A SYSTEMATIC LITERATURE REVIEW

Abstract

Artificial Intelligence (AI) has rapidly transformed education, offering new possibilities for enhancing teaching and learning. This Systematic Literature Review (SLR) aims to examine how AI has been integrated into educational contexts, identify prevailing research trends, and highlight existing gaps for future inquiry. Guided by the PRISMA 2020 framework, the review followed a transparent and replicable process to ensure methodological rigor. The search was conducted using the Scopus database with predefined inclusion and exclusion criteria, yielding 26 peer-reviewed, open-access studies published between 2021 and 2025. The selected studies explore various AI applications, including adaptive instruction, personalized learning, intelligent feedback systems, language education, and teacher professional development. Findings indicate that AI supports personalized and data-driven instruction by improving learner engagement, motivation, and formative assessment practices. Nevertheless, issues related to ethical use, digital infrastructure, and teacher readiness continue to challenge widespread adoption. Overall, this review underscores the potential of AI to enhance evidence-based, equitable, and sustainable educational practices when implemented with pedagogical alignment and ethical consideration.