Artificial Intelligence in Education: A Systematic Review and BibliometricTopic Modeling Analysis of Applications, Challenges, and Future Directions

Artificial intelligence (AI) has moved from a peripheral experimental
technology to a central feature of contemporary educational discourse, prompting a rapidly expanding but fragmented body of scholarship. This paper
presents a systematic review combined with a bibliometric and topic-modelinganalysis of AI-in-education (AIEd) research published between 2010 and 2021. Following PRISMA-guided screening of 3,938 records identified across
Scopus, Web of Science, ERIC, and IEEE Xplore, 158 studies met inclusioncriteria and formed the analytic corpus. Bibliometric mapping (co-authorship, keyword co-occurrence, and citation analysis) was performed alongside Latent
Dirichlet Allocation (LDA) topic modeling of abstracts to surface latent
thematic structures. Results show an accelerating publication trajectory, withoutput roughly doubling every two to three years after 2016, concentrated inasmall set of journals (e.g., Computers & Education, British Journal of
Educational Technology) and countries (United States, China, UnitedKingdom, Australia). Seven latent topics emerged, spanning intelligent tutoringand adaptive learning, learning analytics and educational data mining, conversational agents, assessment automation, personalized learning pathways, AI policy and ethics in higher education, and evolving teacher roles. The most
frequently reported applications were intelligent tutoring systems, adaptivelearning platforms, and learning analytics/predictive dashboards, while themost cited challenges concerned data privacy and ethics, teacher readiness, andequity in access. The review synthesizes these findings into an integratedconceptual map and outlines a future research agenda emphasizingexplainability, teacher-AI collaboration, longitudinal effectiveness evidence, and equity-oriented implementation research.