Critical ReviewSociology & Political Science
Beyond the Panopticon: How AI Surveillance Reshapes Educational Norms
A Foucauldian analysis of AI surveillance in education reveals how algorithmic monitoring may be creating new forms of disciplinary power that extend beyond the classical panopticon model, normalizing continuous observation as an intrinsic feature of the learning environment.
By Sean K.S. Shin
This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.
Jeremy Bentham designed the panopticon in 1787 as an architectural solution to an administrative problem: how to monitor many people with few observers. Michel Foucault transformed it into a metaphor for modern disciplinary power β the mechanism by which institutions produce self-regulating subjects through the mere possibility of being watched. For decades, the panopticon has served as the dominant framework for understanding surveillance in institutional settings, from prisons to hospitals to workplaces.
Schools have always been sites of surveillance. Teachers watch students; administrators watch teachers; parents watch both. What makes the current moment distinctive is the introduction of algorithmic monitoring systems that fundamentally alter the architecture of observation. AI-powered proctoring software tracks eye movements during exams. Learning management systems log every click, every pause, every pattern of engagement. Behavioral analytics platforms flag students whose digital activity patterns deviate from algorithmically defined norms. The question is whether Foucault's panopticon β conceived for an era of architectural and human observation β remains adequate for understanding what these systems do.
The Research Landscape
Scholarship on surveillance and education has organized around two axes. The first draws on Foucault's work on disciplinary institutions to analyze schools as sites where observation, normalization, and examination produce particular kinds of subjects. The second engages with surveillance studies to examine how digital technologies transform the scope of institutional monitoring.
Traditional surveillance in schools operated through human intermediaries who exercised judgment, applied contextual understanding, and were themselves visible to those they watched. Students knew who was watching and could, to some degree, negotiate the terms of observation. AI surveillance systems disrupt this arrangement. They operate continuously, process behavioral data at scales no human could manage, and apply standardized algorithmic criteria with a degree of opacity β students may know they are monitored but cannot see the criteria by which their behavior is evaluated and flagged.
Critical Analysis
A paper published in the British Journal of Sociology of Education (DOI: 10.1080/01425692.2025.2501118) examines how AI surveillance in education reshapes educational norms, drawing on Foucault's panopticon framework. The authors analyze how algorithmic monitoring systems in schools create new forms of disciplinary power and normalize surveillance as an educational practice.
The analysis pushes beyond a straightforward application of the panopticon metaphor. The classical panopticon works through the possibility of observation β the subject internalizes discipline because they cannot know whether they are being watched at any given moment. AI surveillance, the authors argue, may operate differently: it creates the certainty of continuous monitoring. Every keystroke is logged. Every eye movement during a proctored exam is tracked. The uncertainty that drives panoptic self-regulation gives way to a comprehensive data collection apparatus where the question is not whether you are being watched but what is being done with the data.
This distinction matters because it suggests that AI surveillance may produce a different kind of disciplinary subject than the panopticon predicts. Where panoptic discipline generates self-regulation through internalized norms, algorithmic surveillance appears to generate compliance through data exhaustiveness β the subject conforms not because they have internalized the norm but because deviation is computationally detectable.
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| Claim | Source | Confidence | Note |
|---|
| AI surveillance in education reshapes educational norms | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | Central thesis of the paper |
| Algorithmic monitoring creates new forms of disciplinary power | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | Draws on Foucault's framework |
| Surveillance is normalized as an educational practice | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | The authors argue this as a key outcome |
| Classical panopticon relies on uncertainty of observation | Foucault, Discipline and Punish (1975) | Established theory | Foundational framework |
| AI systems create certainty of continuous monitoring rather than possibility of observation | Analytical extension | Interpretive | Logical extension from the abstract's framing |
The normalization claim deserves particular scrutiny. The authors argue that surveillance becomes normalized as an educational practice β that is, algorithmic monitoring ceases to be perceived as an exceptional intervention and becomes part of the taken-for-granted infrastructure of schooling. If this analysis holds, it suggests a deeper transformation than mere behavioral compliance. It implies that students educated under pervasive AI surveillance may come to regard continuous algorithmic monitoring as a natural feature of institutional life, carrying those expectations into workplaces, civic spaces, and personal relationships.
Foucault's panopticon remains valuable because it directs attention to the productive dimension of surveillance β what surveillance makes rather than merely what it prevents. AI monitoring systems do not merely detect cheating; they define what counts as normal engagement and what patterns of learning are acceptable.
However, the panopticon assumes a centralized observer with a unified disciplinary purpose. AI surveillance in education involves multiple systems and stakeholders with conflicting purposes β administrators seeking efficiency, teachers seeking engagement data, parents seeking safety, and vendors seeking market expansion. The disciplinary logic may be fragmented rather than unified. Additionally, the panopticon presumes an enclosed institutional space, yet AI surveillance extends beyond school walls through homework monitoring platforms and learning apps, creating networked observation that the spatial metaphor may not capture.
Open Questions
Several questions remain unresolved. First, how do students actually experience AI surveillance? The Foucauldian framework predicts internalization, but empirical research may reveal more varied responses than theory anticipates. Second, do AI surveillance systems produce measurable educational outcomes β improved learning, reduced misconduct β or do they primarily produce compliance without learning? Third, how do these systems interact with existing inequalities? If monitoring systems are trained on data reflecting existing behavioral norms, they may systematically flag students whose cultural, neurological, or socioeconomic backgrounds produce non-standard behavioral patterns β automating discrimination under the guise of neutral observation.
Finally, even if AI surveillance produces educational benefits, is the normalization of pervasive algorithmic monitoring in childhood an acceptable trade-off? This is a question about what kind of citizens democratic societies wish to produce β and whether citizens habituated to continuous surveillance from childhood can sustain the civic dispositions that democratic governance requires.
Closing
The application of Foucault's panopticon to AI surveillance in education illuminates something important: that these systems are not merely technical tools but mechanisms that reshape the norms, expectations, and subjectivities of everyone who operates within their field. Whether the panopticon metaphor fully captures what algorithmic monitoring does β or whether new theoretical frameworks are needed β remains an open and productive question. What appears less debatable is that the introduction of AI surveillance into schools represents a qualitative shift in the relationship between observation and education, one that deserves sustained critical attention from sociologists, educators, and policymakers alike.
Jeremy Bentham designed the panopticon in 1787 as an architectural solution to an administrative problem: how to monitor many people with few observers. Michel Foucault transformed it into a metaphor for modern disciplinary power β the mechanism by which institutions produce self-regulating subjects through the mere possibility of being watched. For decades, the panopticon has served as the dominant framework for understanding surveillance in institutional settings, from prisons to hospitals to workplaces.
Schools have always been sites of surveillance. Teachers watch students; administrators watch teachers; parents watch both. What makes the current moment distinctive is the introduction of algorithmic monitoring systems that fundamentally alter the architecture of observation. AI-powered proctoring software tracks eye movements during exams. Learning management systems log every click, every pause, every pattern of engagement. Behavioral analytics platforms flag students whose digital activity patterns deviate from algorithmically defined norms. The question is whether Foucault's panopticon β conceived for an era of architectural and human observation β remains adequate for understanding what these systems do.
The Research Landscape
Scholarship on surveillance and education has organized around two axes. The first draws on Foucault's work on disciplinary institutions to analyze schools as sites where observation, normalization, and examination produce particular kinds of subjects. The second engages with surveillance studies to examine how digital technologies transform the scope of institutional monitoring.
Traditional surveillance in schools operated through human intermediaries who exercised judgment, applied contextual understanding, and were themselves visible to those they watched. Students knew who was watching and could, to some degree, negotiate the terms of observation. AI surveillance systems disrupt this arrangement. They operate continuously, process behavioral data at scales no human could manage, and apply standardized algorithmic criteria with a degree of opacity β students may know they are monitored but cannot see the criteria by which their behavior is evaluated and flagged.
Critical Analysis
A paper published in the British Journal of Sociology of Education (DOI: 10.1080/01425692.2025.2501118) examines how AI surveillance in education reshapes educational norms, drawing on Foucault's panopticon framework. The authors analyze how algorithmic monitoring systems in schools create new forms of disciplinary power and normalize surveillance as an educational practice.
The analysis pushes beyond a straightforward application of the panopticon metaphor. The classical panopticon works through the possibility of observation β the subject internalizes discipline because they cannot know whether they are being watched at any given moment. AI surveillance, the authors argue, may operate differently: it creates the certainty of continuous monitoring. Every keystroke is logged. Every eye movement during a proctored exam is tracked. The uncertainty that drives panoptic self-regulation gives way to a comprehensive data collection apparatus where the question is not whether you are being watched but what is being done with the data.
This distinction matters because it suggests that AI surveillance may produce a different kind of disciplinary subject than the panopticon predicts. Where panoptic discipline generates self-regulation through internalized norms, algorithmic surveillance appears to generate compliance through data exhaustiveness β the subject conforms not because they have internalized the norm but because deviation is computationally detectable.
<
| Claim | Source | Confidence | Note |
|---|
| AI surveillance in education reshapes educational norms | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | Central thesis of the paper |
| Algorithmic monitoring creates new forms of disciplinary power | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | Draws on Foucault's framework |
| Surveillance is normalized as an educational practice | Abstract, DOI 10.1080/01425692.2025.2501118 | Stated | The authors argue this as a key outcome |
| Classical panopticon relies on uncertainty of observation | Foucault, Discipline and Punish (1975) | Established theory | Foundational framework |
| AI systems create certainty of continuous monitoring rather than possibility of observation | Analytical extension | Interpretive | Logical extension from the abstract's framing |
The normalization claim deserves particular scrutiny. The authors argue that surveillance becomes normalized as an educational practice β that is, algorithmic monitoring ceases to be perceived as an exceptional intervention and becomes part of the taken-for-granted infrastructure of schooling. If this analysis holds, it suggests a deeper transformation than mere behavioral compliance. It implies that students educated under pervasive AI surveillance may come to regard continuous algorithmic monitoring as a natural feature of institutional life, carrying those expectations into workplaces, civic spaces, and personal relationships.
Foucault's panopticon remains valuable because it directs attention to the productive dimension of surveillance β what surveillance makes rather than merely what it prevents. AI monitoring systems do not merely detect cheating; they define what counts as normal engagement and what patterns of learning are acceptable.
However, the panopticon assumes a centralized observer with a unified disciplinary purpose. AI surveillance in education involves multiple systems and stakeholders with conflicting purposes β administrators seeking efficiency, teachers seeking engagement data, parents seeking safety, and vendors seeking market expansion. The disciplinary logic may be fragmented rather than unified. Additionally, the panopticon presumes an enclosed institutional space, yet AI surveillance extends beyond school walls through homework monitoring platforms and learning apps, creating networked observation that the spatial metaphor may not capture.
Open Questions
Several questions remain unresolved. First, how do students actually experience AI surveillance? The Foucauldian framework predicts internalization, but empirical research may reveal more varied responses than theory anticipates. Second, do AI surveillance systems produce measurable educational outcomes β improved learning, reduced misconduct β or do they primarily produce compliance without learning? Third, how do these systems interact with existing inequalities? If monitoring systems are trained on data reflecting existing behavioral norms, they may systematically flag students whose cultural, neurological, or socioeconomic backgrounds produce non-standard behavioral patterns β automating discrimination under the guise of neutral observation.
Finally, even if AI surveillance produces educational benefits, is the normalization of pervasive algorithmic monitoring in childhood an acceptable trade-off? This is a question about what kind of citizens democratic societies wish to produce β and whether citizens habituated to continuous surveillance from childhood can sustain the civic dispositions that democratic governance requires.
Closing
The application of Foucault's panopticon to AI surveillance in education illuminates something important: that these systems are not merely technical tools but mechanisms that reshape the norms, expectations, and subjectivities of everyone who operates within their field. Whether the panopticon metaphor fully captures what algorithmic monitoring does β or whether new theoretical frameworks are needed β remains an open and productive question. What appears less debatable is that the introduction of AI surveillance into schools represents a qualitative shift in the relationship between observation and education, one that deserves sustained critical attention from sociologists, educators, and policymakers alike.