Learning Machines

In recent years, a novel lexicon—comprising platforms, algorithms, machine learning, and personalisation—has become central to describing an ever-expanding part of social life, including education. Under the rubric of algorithmic management, sociology has begun to explore how these elements relate to contemporary conditions of work and its control.

Building on my previous MSCA research, which developed an initial framework to theorise algorithmic management as the credo and practice of a new class of experts, the LEARNING MACHINES project weaves this notion into a broader research agenda on professional work and organisational change within education. The project charts the competing knowledge claims of professional educators and the occupational groups involved in algorithmically managed instruction, asking: what changes when school life begins to rely on algorithmic procedures and classifications, rather than being organised around the self-image and activities of the teaching profession?

To address this question, the project pursues three interrelated objectives: (1) studying how EdTech start-ups in Italy and Europe seek to bestow worth on what is learned and who has learned it; (2) tracing how teachers interpret, adapt, or resist platform use in classroom practice across Italy and Japan; and (3) analysing how professional regimes shape encounters between algorithmic calculation and professional judgment. Through extended case ethnography across these diverse contexts, the project studies both the production and reception of educational technology—ultimately providing a cogent account of how algorithmic management encounters professional expertise and bureaucratic organisation.

Funded by Italy’s Ministry of University and Research under its MSCA-YOUNG programme.