Still W(AI)ting for the Automation of Teaching: An Exploration of Machine Learning in Swedish Primary Education Using Actor-Network Theory

Autor/inn/en	Sperling, Katarina; Stenliden, Linnéa; Nissen, Jörgen; Heintz, Fredrik
Titel	Still W(AI)ting for the Automation of Teaching: An Exploration of Machine Learning in Swedish Primary Education Using Actor-Network Theory
Quelle	In: European Journal of Education, 57 (2022) 4, S.584-600 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sperling, Katarina) ORCID (Stenliden, Linnéa) ORCID (Nissen, Jörgen) ORCID (Heintz, Fredrik)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0141-8211
DOI	10.1111/ejed.12526
Schlagwörter	Artificial Intelligence; Educational Technology; Technology Uses in Education; Mathematics Instruction; Foreign Countries; Elementary Education; Teaching Methods; Automation; Individualized Instruction; Sweden + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Mathematics lessons; Mathematikunterricht; Ausland; Elementarunterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Individualisierender Unterricht; Schweden
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. The findings are presented in the form of a fictional play in two acts, an "ethnodrama." The ethnodrama highlights controversies in the use of AI in education, such as the lack of transparency in algorithmic decision-making--and how this can play out in real-life learning contexts. The findings of this study contribute to a better understanding of AI in primary education. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01