Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor‐Network Theory

Abstract

Machine learning and other artificial intelligence (AI) technologies are predicted to play a transformative role in primary education, where these technologies for automation and personalization are now being introduced to classroom instruction. This article explores the rationales and practices by which machine learning and AI are emerging in schools. We report on ethnographic fieldwork in Sweden, where a machine learning teaching aid in mathematics, the AI Engine, was tried out by 22 teachers and more than 250 primary education students. By adopting an Actor-Network Theory approach, the analysis focuses on the interactions within the network of heterogeneous actors bound by the AI Engine as an obligatory passage point.The findings show how the actions and accounts emerging within the complex ecosystem of human actors compensate for the unexpected and undesirable algorithmic decisions of the AI Engine. We discuss expectations about AI in education, contradictions in how the AI Engine worked and uncertainties about how machine learning algorithms 'learn' and predict. These factors contribute to our understanding of the potential of automation and personalisation-a process that requires continued re-negotiations. | 585 SPERLING et al. 1 | INTRODUC TI ON VLADIMIR: A-. What are you insinuating? That we have come to the wrong place? ESTRAGON: He should be here. VLADIMIR: He did not say for sure he'd come. ESTRAGON: And if he does not come? VLADIMIR: We'll come back tomorrow. ESTRAGON: And then the day after tomorrow.

Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review

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“…Assuming the 'insider' role as a researcher is likely to have provided access to a more in-depth understanding of the project's development while, at the same time, this role might have led to certain things being taken for granted and other things going unnoticed (cf. Sperling, et al 2022).…”

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