AI evidence extraction

University student perspectives on generative AI: reconfiguring competence, fairness, and authorship in academic work

Wendy Wenxi Hu · Studies in Higher Education · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/03075079.2026.2658738

Methodology & findings

Study design

Qualitative interview study with 17 university students across disciplines

Sample

N = 17, 2 groups

Primary method

Qualitative analysis. Specific statistical or analytical software is not identified in the abstract. The study appears to employ thematic or interpretive analysis of interview data.

Main result

The study identifies three interrelated shifts in how students engage with generative AI: "(1) a redefinition of competence from individual task performance to dialogical judgment in using AI; (2) a renegotiation of authorship as students balance linguistic fluency with the desire to retain ownership of ideas; and (3) an emerging understanding of fairness that emphasizes human – AI collaboration and contextual discretion over universal detection rules." The research demonstrates that "students are not simply adapting to AI but actively reshaping what academic work and learning mean in the age of generative AI."

Reports effect sizes.

Research paradigm

Interpretivist/Qualitative

Author conclusions

The authors conclude that "students are not simply adapting to AI but actively reshaping what academic work and learning mean in the age of generative AI." They further argue that "institutions should revisit their assessment practices, AI policies, and writing support frameworks to better align with the evolving realities of student learning in AI-mediated environments."

Risk of bias

Small, non-representative sample (n=17 students); Single geographic location (Hong Kong); Potential selection bias in student recruitment (volunteers likely more engaged with AI); Limited disciplinary diversity not explicitly detailed; No mention of inter-rater reliability or qualitative validity checks; Interpretive analysis potentially subject to researcher bias; Selection bias: Small sample (n=17) may not be representative of broader student populations; Geographic limitation: Hong Kong setting may limit cross-cultural generalizability; Self-selection bias: Students volunteering for interviews about AI may have particular perspectives; Disciplinary variation: Students from multiple disciplines included, but representation across disciplines unclear; Small sample size (n=17); Geographically limited (Hong Kong only); Potential self-selection bias in interview participants; Disciplinary representation may be unbalanced

Open questions raised

The authors indicate a need for institutions to reconsider educational frameworks in light of AI integration, specifically mentioning the need to rethink educational purpose across Biesta's three dimensions (qualification, socialization, and subjectification) in AI-mediated academic contexts.
The authors note that current views of learning are overly output-oriented and invite "a rethinking of educational purpose across Biesta's three dimensions – qualification, socialization, and subjectification – in light of how students negotiate competence, norms, and authorship in AI-mediated academic work."
The study invites rethinking of educational purpose across Biesta's three dimensions (qualification, socialization, and subjectification) in light of AI-mediated academic work; future research should examine how institutions can align assessment and support frameworks with student realities in AI-mediated learning.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 72%

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