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<p>This article aims to explore the discussions of Artificial Intelligence in Education (AIEd) by analysing the scope of debate and its applications. Defining these discussions is crucial for understanding AI’s impact and guiding its effective integration into educational systems, particularly when general-purpose AI technologies like ChatGPT and DeepSeek are penetrating educational spaces. This study addresses persistent limitations in prior AIEd reviews of narrow temporal spans of five to ten years, limited sets of analysed studies, and exclusionary selection criteria. It examines the evolving AIEd discourse through a systematic review of peer-reviewed literature from 2005 to 2024 on JSTOR, ERIC, ScienceDirect, IEEE, Taylor &amp; Francis, and Google Scholar. The authors adopted Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 (PRISMA 2020) checklist for reporting. Leveraging on computational tools and human expertise, the study analyses 70 peer-reviewed articles using hierarchical clustering with Orange Data Mining followed by manual thematic analysis of identified clusters/themes across three chronological periods: 2005–2011, 2012–2018, and 2019–2024. The review protocol was registered with International Platform of Registered Systematic Review and Meta-Analysis Protocols (no. INPLASY202560042) in June 2025. Findings reveal transition from initial technological optimism to more nuanced implementation considerations. While teacher preparedness and professional development remain an expanding area of research, other key themes developing across periods include the expanding scope of AI from a pedagogical tool to curricular content, infrastructure and environmental requirements for AI adoption, and ethical considerations for AI adoption. Findings indicate that future AIEd discourse will likely centre on developing frameworks to evaluate specialised and general-purpose technology in education, establishing collaborative decision-making processes to enhance AIEd acceptance amongst key stakeholders, and creating sustainable models for continuous professional development of teachers. A limitation of the study is a machine-led synthesis that may influence subsequent manual analysis by predisposing it to particular patterns of interpretation.</p>

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Machine-assisted thematic mapping of AI in education discourse: systematic review from 2005–2024

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  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Harmandeep Kaur,
  • Margarita Pavlova

摘要

This article aims to explore the discussions of Artificial Intelligence in Education (AIEd) by analysing the scope of debate and its applications. Defining these discussions is crucial for understanding AI’s impact and guiding its effective integration into educational systems, particularly when general-purpose AI technologies like ChatGPT and DeepSeek are penetrating educational spaces. This study addresses persistent limitations in prior AIEd reviews of narrow temporal spans of five to ten years, limited sets of analysed studies, and exclusionary selection criteria. It examines the evolving AIEd discourse through a systematic review of peer-reviewed literature from 2005 to 2024 on JSTOR, ERIC, ScienceDirect, IEEE, Taylor & Francis, and Google Scholar. The authors adopted Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 (PRISMA 2020) checklist for reporting. Leveraging on computational tools and human expertise, the study analyses 70 peer-reviewed articles using hierarchical clustering with Orange Data Mining followed by manual thematic analysis of identified clusters/themes across three chronological periods: 2005–2011, 2012–2018, and 2019–2024. The review protocol was registered with International Platform of Registered Systematic Review and Meta-Analysis Protocols (no. INPLASY202560042) in June 2025. Findings reveal transition from initial technological optimism to more nuanced implementation considerations. While teacher preparedness and professional development remain an expanding area of research, other key themes developing across periods include the expanding scope of AI from a pedagogical tool to curricular content, infrastructure and environmental requirements for AI adoption, and ethical considerations for AI adoption. Findings indicate that future AIEd discourse will likely centre on developing frameworks to evaluate specialised and general-purpose technology in education, establishing collaborative decision-making processes to enhance AIEd acceptance amongst key stakeholders, and creating sustainable models for continuous professional development of teachers. A limitation of the study is a machine-led synthesis that may influence subsequent manual analysis by predisposing it to particular patterns of interpretation.