The new era brought forth by AI has prompted many educators to rethink about their roles, beliefs, and practices as they evaluate the affordances and pitfalls of the emerging trend. As expounded by socio-technical studies, technological change can be “influenced by a multiplicity of actors, political and economic interests, and contextual conditions” (
Ahlborg et al., 2019). Such dynamic and dialectical interplays between the social processes and development of technology shape and are shaped by social interactions, power relations, and cultural practices. The consequences of which can be both empowering and alienating (
Ropohl, 1999). In the thought-provoking paper written by
Dieterle et al. (2024), the authors enumerated five divides that can gravely undermine the benefits of integrating technology, in particular, AI in education. They are namely, 1)
access divide, where not all learners and educators have access to the hardware, software, and connectivity necessary to engage with digital tools and learning platforms; 2)
representation divide, where the lack of learner and educator access exacerbates their ability to contribute to data generation, subsequently underrepresenting their voices; 3)
algorithmic divide, where unfettered social datasets can amplify systemic bias that favour certain groups over others; 4)
interpretation divide, where educators’ variegated knowhow and subjective confirmation bias can result in the misinterpretation of data; and 5) c
itizenship divide, where structural stigmas may be perpetuated due to asymmetrical digital participation rate and the ability of disadvantaged groups to accumulate social capital for leapfrogging over time.