Research on the protection and governance of education data privacy in the era of artificial intelligence
Abstract
In artificial intelligence (AI) In this era, the protection and governance of education data privacy has become a key issue of concern, because more and more students' data are collected, analyzed and used for personalized learning and institutional management. This study explores the development prospects of privacy that focuses on the opportunities brought by AI technology. It reviews the current legal framework, ethical considerations and technical solutions, aiming to protect the confidentiality and integrity of education data. This study also investigates the role of AI in solving problems such as data consent, algorithm deviation and transparency, in order to enhance or complicate privacy protection. In addition, this study also proposes a governance model that balances innovation and privacy, ensuring that educational institutions, policy makers and technology developers work together to create a safe and fair learning environment. Through a multidisciplinary approach, This work helps to continuously talk about the ethical use of AI in education, and provides effective practical suggestions for protecting students' privacy in the rapidly developing digital ecosystem.
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Educational AI tools collect student data including writing samples, submission patterns, revision histories, behavioral metadata, and in some proctoring contexts biometric data.
“Liang et al. (2025) document the scope of student data collected by educational AI tools, including writing samples, submission patterns, revision histories, behavioral metadata, and in some proctoring contexts biometric data.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Liang et al. (2025)
AI detection tools function as data collection infrastructure.
“AI detection tools are not just tools for analyzing text. They are data collection infrastructure.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Liang et al. (2025)
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GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
- Authors
- Liang, X., Sun, R., & Wu, Q.
- Affiliations
- Guangxi Polytechnic University (CN)
- Venue
- Second International Conference on Optical Communication and Optoelectronic Technology (OCOT 2025)
- Publisher
- SPIE
- Published
- 2025-11-13
- Pages
- 32
- OpenAlex
- W4416182204
- Cited by
- 0
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- International Conference on Optical Communication and Optoelectronic Technology (OCOT) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Law › Data Privacy and Cybersecurity
- Keywords
- artificial intelligence in education, data governance, student data protection, data consent, algorithmic bias, algorithmic transparency, ethical use of AI, personalized learning, data confidentiality and integrity
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