Cited by 26Digital Education and SocietyEthics and Social Impacts of AIFoucault, Power, and Ethics

Revisiting Foucault’s panopticon: how does AI surveillance transform educational norms?

Open access
Dai, Ruixun, Thomas, Matthew Krehl Edward, Rawolle, Shaun·2025·British Journal of Sociology of Education

Abstract

This article applies Foucault’s theories of surveillance, disciplinary power, and normalisation to examine the shifting power dynamics in AI-mediated education. Drawing on qualitative responses from 27 English and Chinese-speaking stakeholders, including students, teachers, administrators and parents with no geographical restrictions, this study investigates how AI surveillance reshapes behaviours and perceptions and identifies emergent norms in education. Thematic analysis revealed four key themes: normalising ubiquitous surveillance and behavioural control, prioritising efficiency over autonomy, reaffirming the importance of human elements in AI-assisted education, and ensuring human-AI collaboration. Machine learning is not neutral but an active agent of algorithmic control, reflecting a post-panoptic power structure. It introduces new forms of disciplinary power, encouraging behaviours aligned with efficiency at the expense of autonomy and privacy. Despite these shifts, the findings underscore the vital role of human engagement, pointing to a future where human agency remains central in regulating and complementing AI in education.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01425692.2025.2501118

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

1 of the 58 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. Milton Park, Abingdon, Oxon, UK: Routledge.
  2. Biesta, G., and L. Stenhouse. 2020. "Digital First or Education First? Why we Shouldn't Let a Virus Undermine Our Educational Artistry. " Journal of Curriculum and Supervision, 4 (1): 43-51.
  3. Gutting, G. 2005. Foucault: A Very Short Introduction. Vol. 122. Oxford, UK: Oxford University Press.
  4. Milton Park, Abingdon, Oxon, UK: Routledge.

Cited by

Sorted by citations

Authors
Dai, R., Thomas, M. K. E., & Rawolle, S.
Affiliations
Deakin University (AU)
Corresponding author
Dai, Ruixun
Venue
British Journal of Sociology of Education
Published
2025-05-09
Volume
46
Issue
5
Pages
650-668
ISSN
  • 0142-5692 (ISSN-L)
  • 1465-3346
OpenAlex
W4410224644
Cited by
17
References
58
Citations by year
2026: 14 · 2025: 3
FWCI
17.82
Type
journal article
Language
en
Open access
Locations
  • British Journal of Sociology of Education (publishedVersion, cc-by-nc-nd)
  • Figshare (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Foucault, Power, and Ethics
Keywords
AI monitoring, panopticon, Michel Foucault, disciplinary power, normalization, algorithmic control, student independence, privacy, human-AI collaboration, educational standards, power in education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review