Analyzing Security and Privacy Challenges in Generative AI Usage Guidelines for Higher Education
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913912302075904 |
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| author | Ng, Bei Yi Li, Jiarui Tong, Xinyuan Ye, Kevin Yenne, Gauthami Chandrasekaran, Varun Li, Jingjie |
| author_facet | Ng, Bei Yi Li, Jiarui Tong, Xinyuan Ye, Kevin Yenne, Gauthami Chandrasekaran, Varun Li, Jingjie |
| contents | Educators and learners worldwide are embracing the rise of Generative Artificial Intelligence (GenAI) as it reshapes higher education. However, GenAI also raises significant privacy and security concerns, as models and privacy-sensitive user data, such as student records, may be misused by service providers. Unfortunately, end-users often have little awareness of or control over how these models operate. To address these concerns, universities are developing institutional policies to guide GenAI use while safeguarding security and privacy. This work examines these emerging policies and guidelines, with a particular focus on the often-overlooked privacy and security dimensions of GenAI integration in higher education, alongside other academic values. Through a qualitative analysis of GenAI usage guidelines from universities across 12 countries, we identify key challenges and opportunities institutions face in providing effective privacy and security protections, including the need for GenAI safeguards tailored specifically to the academic context. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Security and Privacy Challenges in Generative AI Usage Guidelines for Higher Education Ng, Bei Yi Li, Jiarui Tong, Xinyuan Ye, Kevin Yenne, Gauthami Chandrasekaran, Varun Li, Jingjie Human-Computer Interaction Computers and Society Educators and learners worldwide are embracing the rise of Generative Artificial Intelligence (GenAI) as it reshapes higher education. However, GenAI also raises significant privacy and security concerns, as models and privacy-sensitive user data, such as student records, may be misused by service providers. Unfortunately, end-users often have little awareness of or control over how these models operate. To address these concerns, universities are developing institutional policies to guide GenAI use while safeguarding security and privacy. This work examines these emerging policies and guidelines, with a particular focus on the often-overlooked privacy and security dimensions of GenAI integration in higher education, alongside other academic values. Through a qualitative analysis of GenAI usage guidelines from universities across 12 countries, we identify key challenges and opportunities institutions face in providing effective privacy and security protections, including the need for GenAI safeguards tailored specifically to the academic context. |
| title | Analyzing Security and Privacy Challenges in Generative AI Usage Guidelines for Higher Education |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2506.20463 |