Machine learning – A new kind of cultural tool? A “recontextualisation” perspective on machine learning + interprofessional learning

Abstract

The paper argues that (a) Machine Learning (ML) constitutes a cultural tool capable of learning through perceiving patterns in data, (b) the kind of learning ML is capable of nevertheless constitutes a more circumscribed kind of learning compared with how that concept has been interpreted in sociocultural (S-c) theory; and, (c) the development of ML is therefore further extending and distributing the complex relationship between human and machine cognition and learning. The paper explores these contentions by firstly, providing a broad-based account of the conception of cultural tools in S-c Theory. Secondly, offering a genealogy of ML, including the model of learning that underpins ML and highlights the challenge that a cultural too capable of some kind of learning presents for the extant S-c conception of a cultural tool. Thirdly, identifying the new human-machine working-learning problem the ML model of learning is generating. Finally, argues the concept of recontextualization offers a way to address that problem by providing a holistic perspective on the relationship between ML and IPL models of learning. In making this argument the paper distinguishes between the ML predictive and the Chat GPT answer to question(s) model of learning.