Cyberethics, Data Surveillance, and the Classroom: Addressing Corporate Control of Generative AI to Protect Student Learning Environments

Wilhelmina Randtke; Kevin Reagan

Cyberethics, Data Surveillance, and the Classroom: Addressing Corporate Control of Generative AI to Protect Student Learning Environments

Research output: Contribution to conference › Poster › peer-review

Abstract

Generative AI (GenAI) is rapidly being incorporated into higher education assignments, often with lesson plans promoted without mention of data privacy (Walker et al., 2023). This proposal argues that unlike previous educational technologies, GenAI presents a significantly elevated risk to student privacy and intellectual freedom due to industry consolidation.

The massive resources required to train GenAI algorithms create conditions for a natural monopoly, consolidating control among a few very large technology corporations (e.g., Alphabet/Google, Microsoft, Meta) (Vipra & Myers West, 2023). This contrasts sharply with past educational technologies, which were often managed in-house or by much smaller educational technology firms. GenAI's reliance on continuous, two-way data exchange via APIs means student behavioral data is shared with companies whose core business model is rooted in surveillance capitalism.

Consequently, requiring GenAI use forces students into an ethically fraught situation: they must consent to corporate surveillance and give up intellectual freedom as a condition for course completion, often through complex and ineffective terms of service (Tokson, 2024). We outline the structural privacy challenges and provide practical, transdisciplinary strategies—including leveraging privacy literacy instruction from librarians—to empower students and mitigate the ethical risks of required GenAI use in the classroom.

Tokson, M. (2024). Government purchase of private data. Wake Forest Law Review, 59(1), 269–324. https://www.wakeforestlawreview.com/wp-content/uploads/2024/10/Tokson_Final.pdf

Vipra, J. & Myers West, S. (2023). Computational power and AI. AI Now Institute. https://ainowinstitute.org/wp-content/uploads/2023/09/AI-Now_Computational-Power-an-AI.pdf

Walker, K. L., Bodendorf, K., Kiesler, T., de Mattos, G., Rostom, M., & Elkordy, A. (2023). Compulsory technology adoption and adaptation in education: A looming student privacy problem. Journal of Consumer Affairs, 57(1), 445-478. https://doi.org/10.1111/joca.12506

Original language	American English
State	Published - Feb 26 2026
Event	Scholarship of Teaching and Learning Commons Conference - Savannah, GA, United States Duration: Feb 26 2026 → Feb 28 2026

Conference

Conference	Scholarship of Teaching and Learning Commons Conference
Country/Territory	United States
City	Savannah, GA
Period	02/26/26 → 02/28/26

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 16 Peace, Justice and Strong Institutions

Scopus Subject Areas

Education
Economics and Econometrics
Artificial Intelligence
Management of Technology and Innovation

Disciplines

Economics
Artificial Intelligence and Robotics

Cite this

@conference{50e304d2a3cb4aa3933ff2a09a81da50,

title = "Cyberethics, Data Surveillance, and the Classroom: Addressing Corporate Control of Generative AI to Protect Student Learning Environments",

abstract = "Generative AI (GenAI) is rapidly being incorporated into higher education assignments, often with lesson plans promoted without mention of data privacy (Walker et al., 2023). This proposal argues that unlike previous educational technologies, GenAI presents a significantly elevated risk to student privacy and intellectual freedom due to industry consolidation. The massive resources required to train GenAI algorithms create conditions for a natural monopoly, consolidating control among a few very large technology corporations (e.g., Alphabet/Google, Microsoft, Meta) (Vipra \& Myers West, 2023). This contrasts sharply with past educational technologies, which were often managed in-house or by much smaller educational technology firms. GenAI's reliance on continuous, two-way data exchange via APIs means student behavioral data is shared with companies whose core business model is rooted in surveillance capitalism. Consequently, requiring GenAI use forces students into an ethically fraught situation: they must consent to corporate surveillance and give up intellectual freedom as a condition for course completion, often through complex and ineffective terms of service (Tokson, 2024). We outline the structural privacy challenges and provide practical, transdisciplinary strategies—including leveraging privacy literacy instruction from librarians—to empower students and mitigate the ethical risks of required GenAI use in the classroom. Tokson, M. (2024). Government purchase of private data. Wake Forest Law Review, 59(1), 269–324. https://www.wakeforestlawreview.com/wp-content/uploads/2024/10/Tokson\_Final.pdf Vipra, J. \& Myers West, S. (2023). Computational power and AI. AI Now Institute. https://ainowinstitute.org/wp-content/uploads/2023/09/AI-Now\_Computational-Power-an-AI.pdf Walker, K. L., Bodendorf, K., Kiesler, T., de Mattos, G., Rostom, M., \& Elkordy, A. (2023). Compulsory technology adoption and adaptation in education: A looming student privacy problem. Journal of Consumer Affairs, 57(1), 445-478. https://doi.org/10.1111/joca.12506",

author = "Wilhelmina Randtke and Kevin Reagan",

year = "2026",

month = feb,

day = "26",

language = "American English",

note = "Scholarship of Teaching and Learning Commons Conference ; Conference date: 26-02-2026 Through 28-02-2026",

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TY - CONF

T1 - Cyberethics, Data Surveillance, and the Classroom

T2 - Scholarship of Teaching and Learning Commons Conference

AU - Randtke, Wilhelmina

AU - Reagan, Kevin

PY - 2026/2/26

Y1 - 2026/2/26

N2 - Generative AI (GenAI) is rapidly being incorporated into higher education assignments, often with lesson plans promoted without mention of data privacy (Walker et al., 2023). This proposal argues that unlike previous educational technologies, GenAI presents a significantly elevated risk to student privacy and intellectual freedom due to industry consolidation. The massive resources required to train GenAI algorithms create conditions for a natural monopoly, consolidating control among a few very large technology corporations (e.g., Alphabet/Google, Microsoft, Meta) (Vipra & Myers West, 2023). This contrasts sharply with past educational technologies, which were often managed in-house or by much smaller educational technology firms. GenAI's reliance on continuous, two-way data exchange via APIs means student behavioral data is shared with companies whose core business model is rooted in surveillance capitalism. Consequently, requiring GenAI use forces students into an ethically fraught situation: they must consent to corporate surveillance and give up intellectual freedom as a condition for course completion, often through complex and ineffective terms of service (Tokson, 2024). We outline the structural privacy challenges and provide practical, transdisciplinary strategies—including leveraging privacy literacy instruction from librarians—to empower students and mitigate the ethical risks of required GenAI use in the classroom. Tokson, M. (2024). Government purchase of private data. Wake Forest Law Review, 59(1), 269–324. https://www.wakeforestlawreview.com/wp-content/uploads/2024/10/Tokson_Final.pdf Vipra, J. & Myers West, S. (2023). Computational power and AI. AI Now Institute. https://ainowinstitute.org/wp-content/uploads/2023/09/AI-Now_Computational-Power-an-AI.pdf Walker, K. L., Bodendorf, K., Kiesler, T., de Mattos, G., Rostom, M., & Elkordy, A. (2023). Compulsory technology adoption and adaptation in education: A looming student privacy problem. Journal of Consumer Affairs, 57(1), 445-478. https://doi.org/10.1111/joca.12506

AB - Generative AI (GenAI) is rapidly being incorporated into higher education assignments, often with lesson plans promoted without mention of data privacy (Walker et al., 2023). This proposal argues that unlike previous educational technologies, GenAI presents a significantly elevated risk to student privacy and intellectual freedom due to industry consolidation. The massive resources required to train GenAI algorithms create conditions for a natural monopoly, consolidating control among a few very large technology corporations (e.g., Alphabet/Google, Microsoft, Meta) (Vipra & Myers West, 2023). This contrasts sharply with past educational technologies, which were often managed in-house or by much smaller educational technology firms. GenAI's reliance on continuous, two-way data exchange via APIs means student behavioral data is shared with companies whose core business model is rooted in surveillance capitalism. Consequently, requiring GenAI use forces students into an ethically fraught situation: they must consent to corporate surveillance and give up intellectual freedom as a condition for course completion, often through complex and ineffective terms of service (Tokson, 2024). We outline the structural privacy challenges and provide practical, transdisciplinary strategies—including leveraging privacy literacy instruction from librarians—to empower students and mitigate the ethical risks of required GenAI use in the classroom. Tokson, M. (2024). Government purchase of private data. Wake Forest Law Review, 59(1), 269–324. https://www.wakeforestlawreview.com/wp-content/uploads/2024/10/Tokson_Final.pdf Vipra, J. & Myers West, S. (2023). Computational power and AI. AI Now Institute. https://ainowinstitute.org/wp-content/uploads/2023/09/AI-Now_Computational-Power-an-AI.pdf Walker, K. L., Bodendorf, K., Kiesler, T., de Mattos, G., Rostom, M., & Elkordy, A. (2023). Compulsory technology adoption and adaptation in education: A looming student privacy problem. Journal of Consumer Affairs, 57(1), 445-478. https://doi.org/10.1111/joca.12506

M3 - Poster

Y2 - 26 February 2026 through 28 February 2026

ER -

Cyberethics, Data Surveillance, and the Classroom: Addressing Corporate Control of Generative AI to Protect Student Learning Environments

Abstract

Conference

UN SDGs

Scopus Subject Areas

Disciplines

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