Privacy and data protection was a critical governance theme, raised by over half the expert panel¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person · recheck by 2031: baselines and conditions change

Subclaims¶

q2 i? Over half the panel (55%) explicitly raised privacy or data security concerns, recommending institutions set clear rules on what data may be shared with GenAI platforms and that inputs be anonymized. → Crompton 2026

Evidence¶

Crompton 2026¶

Crompton, H., Burke, D., Nickel, C., Bozkurt, A., Miao, F., Sharples, M., Greene, J. A., Parsons, D., Gill-Simmen, L., Edmett, A., Pegrum, M., de Waard, I., Bonk, C. J., Garcia, M. B., Curry, J. H., Lindsey, L., Yang, M., Marshall, S., Bali, M., Deutsch, N., le Roux, S., Benali, M., Bin Samsudin, M. A., Tinmaz, H., Bernacki, M. L., van Wyk, M., Singh, L., Chigona, A., Eaton, L., Xiao, J., Velander, J., Kim, J., Bellas, F., Rajalakshmi, R., de Bem Machado, A., Palalas, A., & Yu, S. (2026). Governing generative AI in higher education: a global Delphi study on policy and practice. International Journal of Educational Technology in Higher Education, 23(21). https://doi.org/10.1186/s41239-026-00602-z

q2 · i? · qualitative · r3

Grounded-coding finding from the Delphi panel: "Over half the panel (55%)" explicitly raised privacy or data security. Panelists proposed anonymizing inputs, approving only GenAI technologies meeting security standards, and educating users about safeguarding their data.

"Over half the panel (55%) explicitly raised privacy or data security in their comments, reflecting deep concern about how GenAI technologies handle sensitive information."

Discussion¶

Related Claims¶

• Students raised four categories of concern about potential misuse of MMLA data: judgment, privacy, career impact, and academic integrity — related
• Teachers raise big-picture concerns about GenAI including surveillance, over-reliance, reduced teacher collaboration, job replacement, and training needs — related
• PGR students strongly opposed uploading human-participant or sensitive qualitative data to GenAI platforms or using GenAI to interpret such data — a narrower finding that bears on this claim
• Universities converge on privacy-oriented data guardrails, warning against entering sensitive data into consumer AI tools and encouraging institutionally approved services — related
• AI-related changes to consumer software threaten the anonymity of remotely collected research data — related
• Expert panelists overwhelmingly favor a hybrid governance model combining formal policies and flexible guidelines for GenAI in higher education — related
• Researchers may need to treat all remotely collected data as potentially identifiable personal data — related
• Institutions diverge in tooling strategy between protected, institutionally hosted AI environments and consumer tools governed by risk controls — related
• Equitable access emerged as a prominent governance theme, with one-third of panelists explicitly mentioning equity or inclusion — related