Inclusivity, decoloniality, and artificial intelligence: A critical intervention for university classrooms

Kehinde Clement Lawrence

2026

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DOI 10.51986/ijcc-2026.vol1.06
Data confidence 70 medium
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Abstract

This conceptual review interrogates the rapid adoption of artificial intelligence (AI) in university classrooms through the dual lenses of inclusivity and decoloniality. It argues that while AI may support teaching and learning, its uncritical adoption risks reproducing epistemic injustice, digital inequality, and exclusionary pedagogical practices, particularly for students whose languages, knowledges, and histories were marginalised by colonial higher education. Drawing together decolonial scholarship (Mignolo & Walsh, 2018; Santos, 2014; Ndlovu-Gatsheni, 2018; Mbembe, 2016), critical AI and data studies (Benjamin, 2019; Noble, 2018; Eubanks, 2018; Crawford, 2021; Couldry & Mejias, 2019; Buolamwini & Gebru, 2018), and the literature on epistemic injustice and AI in higher education (Fricker, 2007; Selwyn, 2022; Williamson & Eynon, 2020; Zembylas, 2023), the paper employs an integrative critical review methodology to develop five interlocking arguments: that AI is an epistemological actor, not a neutral aid; that algorithmic bias is a pedagogical issue rather than a purely technical one; that uneven access stratifies rather than democratises; that standardisation crowds out plural ways of knowing; and that classroom AI ethics must be relational rather than procedural. These arguments are synthesised into an original Inclusive-Decolonial AI Pedagogy (IDAP) framework, presented as a four-quadrant diagnostic that orients universities toward justice-centred, plural, and accountable AI use.