Exploring Student Discourses on AI-Informed Citizenship in Mathematics Education

Resumo

This article explores how upper secondary students position themselves when mathematics education engages in critical, ethical and societal perspectives on AI and data driven practices based on machine learning and online digital traces. Mathematics lessons were designed and conducted to cover how data on citizens are used in machine learning to build mathematical prediction models. Such models are becoming increasingly capable due to the growing availability of digital data and advances in AI technology, and they play an increasingly prominent role in society. The lessons were designed to elucidate connections between properties in the data and properties in models, and what societal ramifications those models can have. A Foucault-inspired discourse analysis was conducted on classroom video recordings and post-intervention interviews. The analysis focused on mathematics, data, and ethics in relation to data-driven practices in society. Four discourses were construed in the discourse analysis: Choices in AI modeling have a societal impact, Accuracy is the solution to ethical issues, Data practices in society are unchallengeable, and Mathematics and ethics are incommensurable. The first discourse portrays how an amalgamation of data, mathematical, and AI literacies are utilized to enable identification of unfair data driven practices. The other three discourses portray in various ways resistance towards formulating such critique. The discourses reflect students' understanding, shaped in and by social contexts--such as societal discussions on digital technology and mathematics--which in turn may become essential in the future for navigating how to effectively teach AI-informed citizenship in mathematics education.

Dados da obra

Tipo

Artigo

Periódico

ZDM: Mathematics Education

Editora

Springer Science+Business Media

Ano

2026 (2026-09-17)

Idioma

en

Palavras-chave

Secondary School Students; Mathematics Education; Artificial Intelligence; Technology Uses in Education; Educational Technology; Prediction; Mathematical Models; Citizenship; Ethics; Data Use; Social Influences; Accuracy; Foreign Countries; student discourse; critical perspectives on AI; data-driven practice; machine learning; prediction model; AI ethics; digital traces; AI literacy; data literacy; mathematical literacy; Michel Foucault; high school students; societal impact of AI

Temas

Tecnologias Digitais

DOI

10.1007/s11858-026-01840-1

ISSN

1863-9690, 1615-679X, 1863-9704

Licença

cc-by

Acesso

Acesso aberto

Fontes

ERIC — Education Resources Information Center; OpenAlex

Na Biblioteca desde

01/10/2026 · verificada em 03/10/2026