How AI works: reconfiguring lifelong learning - Palenski, T; Hills, L; Unnikrishnan, S; Eynon, R (Springer Nature, 2024)
Lifelong learning is a current policy focus in many countries, with AI technologies promoted as both the motivation for the need for lifelong learning (due to its assumed role in social change) and as an important way to ‘deliver’ learning across the life course. Such policies tend to be instrumental and technologically deterministic, and there is a need to properly theorize the relationships between AI and lifelong learning to better inform policy and practice. In this paper, we examine the ways that academic communities conceptualize AI and lifelong learning, based on a thematic analysis of existing academic literature in contexts beyond formal education. We identify three groups of research, which vary according to their engagement with theories of learning and AI technology and how AI ‘works’. In group 1 (working AI), AI is assumed to contribute to increased efficiency of humans and learning; in group 2 (working with AI), AI is implemented and conceptualized as a peer or colleague; and in group 3 (reconfiguring AI), AI is viewed as part of a wider reconfiguration of humans and their contexts. This latter group, though least well represented in the literature, holds promise in advancing a postdigital research agenda that focuses not solely on how AI works to increase efficiency, but how people are increasingly working, learning, and living with AI, thus moving beyond exclusively instrumental, economic, and technologically deterministic concerns.