Generative AI as meaning-mediating infrastructure in education: a critical integrative review of inclusion, exclusion, and semantic transduction

This article offers a critical integrative review of current debates on generative AI (GenAI), educational inequality, and inclusion/exclusion. Rather than treating GenAI as either a neutral access tool or a simple amplifier of disadvantage, it analyses AI as meaning-mediating infrastructure that reshapes the communicative forms through which learners become addressable, recognised, supported, and evaluated. The review synthesises work from inclusive education, digital inequality, AI in education, critical edtech, science and technology studies, philosophy of technology, and Luhmannian systems theory. It argues that GenAI matters less through isolated outputs than through semantic transduction: the socio-technical reformatting of prompts, rubrics, feedback, and disciplinary genres into communicatively plausible forms. It treats inclusion and exclusion as selective, communicatively regulated processes rather than as simple opposites or as properties of individual ability. Across the reviewed literature, three recurring sites of AI-mediated inclusion/exclusion emerge: access and capability divides, misrecognition through templates of “good” performance, and normative drift in pedagogy, authorship, and assessment. This article clarifies how an autopoietic ecological orientation extends systems theory by observing AI-mediated education as a coupled ecology of classroom routines, household capacities, platform interfaces, assessment programmes, policy rules, and vendor infrastructures. It concludes that individualised AI literacy is necessary but insufficient, and proposes an infrastructural governance agenda centred on policy transparency, inclusive design and procurement, participation and redress, and iterative audit and repair.