Artificial Intelligence in Education: A Systematic Literature Review of Applications, Challenges, and Future Research Directions

Abstract

Artificial Intelligence (AI) has increasingly transformed educational practices, creating new opportunities for personalized learning, intelligent tutoring, automated assessment, learning analytics, and instructional support. However, the rapid adoption of AI, particularly Generative AI and Large Language Models, also raises concerns regarding privacy, algorithmic bias, academic integrity, technological dependence, and teacher readiness. This study aims to systematically review the existing literature on Artificial Intelligence in Education (AIED) by examining its major applications, benefits, challenges, and future research directions. A Systematic Literature Review (SLR) was conducted using the Scopus database and guided by the PRISMA 2020 framework. Relevant studies were identified, screened, and analyzed using thematic synthesis. The findings indicate that AI applications are primarily concentrated in personalized and adaptive learning, intelligent tutoring systems, teaching support, assessment and feedback, learning analytics, and educational content generation. The review also highlights significant challenges related to data privacy, algorithmic fairness, transparency, academic integrity, digital inequality, and AI literacy. Research gaps remain in K–12 education, developing countries, inclusive education, culturally diverse contexts, and longitudinal evaluation of AI's educational impacts. The study recommends strengthening AI literacy, responsible AI governance, teacher professional development, and human-AI collaboration. Future research should prioritize evidence-based, ethical, inclusive, and human-centered approaches to ensure that AI contributes meaningfully to educational quality and learning outcomes.