Still w( AI )ting for the automation of teaching

An exploration of machine learning in Swedish primary education using Actor‐Network Theory

Dados Bibliográficos

ID21446428
AutoresKatarina Sperling (0000-0003-0664-3640, Department of Behavioural Sciences and Learning Linköping University Norrköping Sweden, autor correspondente), Linnea Stenliden (0000-0002-3115-9060, Department of Behavioural Sciences and Learning Linköping University Norrköping Sweden), Jörgen Nissen (0000-0002-3979-1343, Department of Behavioural Sciences and Learning Linköping University Norrköping Sweden), Fredrik Heintz (0000-0002-9595-2471, Department of Computer and Information Science Linköping University Linköping Sweden)
Ano2022
Volume57
Fascículo4
Páginas584-600
Data de publicação2022-12-01
Open AccessSim
TipoARTICLE
PeriódicoEuropean Journal of Education (JOURNAL)
Identificadores do periódicoISSN: 0141-8211 • E-ISSN: 1465-3435
EditoraWiley (PUBLISHER • GB)
DOI10.1111/ejed.12526
OpenAlexW4307889336
IdiomaEN
Citações recebidas18
Referências citadas33

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

Automation · Computer security · Knowledge management · Negotiation · Pedagogy · Personalization · Social science · Sociology · Transformative learning · Transparency (behavior) · World Wide Web · Artificial Intelligence · Computer Science · Engineering · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Teaching and Learning Programming

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