Panelists strongly agreed human oversight must remain central in academic decisions involving GenAI, with clear accountability assignments¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Panelists strongly agreed that human oversight must remain central in any use of GenAI for academic decisions or evaluative processes, and that institutions should specify who is accountable when GenAI is deployed. → 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: "Panelists strongly agreed that human oversight must remain central" for academic decisions involving GenAI. Panelists proposed that significant GenAI outputs be reviewed and validated by a human, with institutions specifying who is accountable.

"Panelists strongly agreed that human oversight must remain central in any use of GenAI for academic decisions or evaluative processes."

Discussion¶

Related Claims¶

• Academic integrity was the panel's most frequently cited GenAI policy concern, requiring clear definitions of misconduct and enforceable consequences — related
• LLM-assisted inductive qualitative coding carries risks of superficial themes, broad or redundant codes, and hallucinated interpretations, so LLMs should augment rather than replace human researchers — related
• Human guidance remains essential for ethical grounding when integrating AI with situated learning, including resisting complacency toward generative AI outputs — related
• In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — related
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• Practitioners hold near-universal commitments to human oversight and critical evaluation of AI outputs — a broader claim this one bears on
• Les citations bibliographiques produites par ChatGPT sont inexistantes ou incorrectes dans 23 % des cas, compromettant le circuit de validation scientifique — related
• Students exercise ethical agency in GenAI use through human-AI collaboration strategies, personal ethical frameworks, strategic calibration of use, and resistance to over-reliance — related
• Human oversight in co-pilot mode improved Agent Laboratory research output quality ratings from 3.8/10 to 4.38/10 — a narrower finding that bears on this claim
• Les LLMs utilisés comme juges d'évaluation scientifique présentent douze types de biais qui désavantagent les productions s'écartant des canons dominants — related
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