Systematic literature review on artificial intelligence in education (AIED): proposing a conceptual structure

Abstract

Purpose We conduct this systematic literature review to propose a conceptual structure for the Artificial Intelligence in Education (AIED) field. By analyzing how research themes organize, interconnect, and evolve, we overcome the keyword limitations of past studies to clarify the field's current state and managerial implications. Design/methodology/approach Following the PRISMA protocol, we analyzed 127 articles from Web of Science and Scopus. We extracted bigrams directly from abstracts and used Bibliometrix for a thematic mapping analysis, classifying them into four quadrants by their centrality and density. Findings We identified ten thematic clusters constituting the AIED conceptual structure. The field has rapidly expanded since 2020, significantly influenced by the public release of ChatGPT. Our results highlight the tensions between technological efficiency and pedagogical concerns. Research limitations/implications Our review is limited to articles indexed in Web of Science and Scopus. Practical implications We propose managerial implications for defining policies on adoption, ethical use and maintenance of AI systems in schools and educational environments. This includes establishing institutional AIED management policies with a multidisciplinary committee for supervising implementation and conducting “AI literacy” programs for managers and faculty. Originality/value Our study offers a unique and more comprehensive understanding of the AIED conceptual structure by moving beyond traditional keyword analysis. We apply a novel thematic mapping approach, revealing implicit conceptual relationships within literature. This allows for a deeper analysis of the field's evolution and interconnected themes, leading to suggested managerial implications regarding AIED adoption and use.

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