The diffusion of information-and-communication technologies (ICT) and, increasingly, artificial intelligence (AI) across higher education—the transition from ICT-supported teaching toward intelligent, data-driven, and sustainable educational applications—has produced a large but fragmented literature, and no systematic account yet maps the thematic structure of this AI–ICT intersection quantitatively. This study addresses that gap with a systematic literature review coupled to a reproducible topic-modeling pipeline. Following the PRISMA 2020 protocol, we searched six bibliographic databases and screened the results down to a corpus of 126 peer-reviewed journal articles published between 2021 and 2026. The corpus was analyzed with a Latent Dirichlet Allocation model whose number of topics and hyper-parameters were selected by a many-objective NSGA-III optimization that jointly balances topic coherence, separation, distributional equity, generalization, and stability, complemented by an overlap-aware soft assignment that quantifies cross-cutting studies. The analysis recovers three comparably sized themes—institutional adoption and digital transformation; generative AI, instructional design, and critical thinking; and AI for teaching quality, performance, and intelligent systems—each accounting for a comparable share of the corpus. Interpreted through an ICT–AGI framework used as an analytical lens, scholarly attention spreads across the framework’s administrative, generative-AI, and learning-facing dimensions rather than concentrating on any one, and about a quarter of the studies bridge more than one theme; the strongest coupling links institutional adoption to teaching quality and the weakest links generative AI to teaching quality, a difference that a null-model test shows is not statistically robust, so we report it as suggestive rather than as a firmly established gap. The study offers an evidence-based thematic map of the AI–ICT intersection in higher education and a transferable, many-objective methodology for machine-assisted evidence synthesis.
From ICT to Intelligent Education: A Many-Objective Topic-Modeling Systematic Review of the Artificial Intelligence–ICT Intersection in Higher Education
Marengo A.;Santamato V.;
2026-01-01
Abstract
The diffusion of information-and-communication technologies (ICT) and, increasingly, artificial intelligence (AI) across higher education—the transition from ICT-supported teaching toward intelligent, data-driven, and sustainable educational applications—has produced a large but fragmented literature, and no systematic account yet maps the thematic structure of this AI–ICT intersection quantitatively. This study addresses that gap with a systematic literature review coupled to a reproducible topic-modeling pipeline. Following the PRISMA 2020 protocol, we searched six bibliographic databases and screened the results down to a corpus of 126 peer-reviewed journal articles published between 2021 and 2026. The corpus was analyzed with a Latent Dirichlet Allocation model whose number of topics and hyper-parameters were selected by a many-objective NSGA-III optimization that jointly balances topic coherence, separation, distributional equity, generalization, and stability, complemented by an overlap-aware soft assignment that quantifies cross-cutting studies. The analysis recovers three comparably sized themes—institutional adoption and digital transformation; generative AI, instructional design, and critical thinking; and AI for teaching quality, performance, and intelligent systems—each accounting for a comparable share of the corpus. Interpreted through an ICT–AGI framework used as an analytical lens, scholarly attention spreads across the framework’s administrative, generative-AI, and learning-facing dimensions rather than concentrating on any one, and about a quarter of the studies bridge more than one theme; the strongest coupling links institutional adoption to teaching quality and the weakest links generative AI to teaching quality, a difference that a null-model test shows is not statistically robust, so we report it as suggestive rather than as a firmly established gap. The study offers an evidence-based thematic map of the AI–ICT intersection in higher education and a transferable, many-objective methodology for machine-assisted evidence synthesis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
