Critical Data and Artificial Intelligence literacies in academic practice

While datafication has long been debated in higher education, discussions around AI have recently intensified. Much of the discourse oversimplifies AI, neglecting the provenance, ethics, and reliability of its underpinning data. Instead, media narratives often emphasise AI’s supposed ‘disruption’ of society. However, AI comprises diverse data-processing systems rather than a singular entity, making critical data understanding essential. This chapter is framed by an integrative literature review, a mapping of AI definitions, and a survey to academics. As automated decisions and algorithmic systems increasingly shape our world, datafication is already embedded in social structures, including education. Yet, discussions on AI in education frequently perpetuate ‘AI hype’. We argue for the necessity of critical data and AI literacies to address these challenges. While these literacies alone cannot dismantle power imbalances, they enable individuals to recognise and respond to them. We advocate a collaborative, open educational approach, grounded in data justice, to develop critical AI literacies among academics, students, and organisations.