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AI evidence extraction

Generative AI in Academic Writing: Writing Support, Human Agency, and Institutional Governance

Kruttika Sutrave, Tamara L. Stachowicz, Joshua Scotto Divetta · Journal of the Association for Information Systems · 2026

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

10/10
Relevance
1/4
Quality (LMQS)
I
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Systematic literature review synthesizing empirical studies on GenAI use in academic writing, organized around technical, social, and environmental dimensions of AI-mediated writing systems.

Main result

The literature shows that "GenAI supports writing, feedback, and assessment tasks." However, the review also found that "GenAI also reshapes the social and institutional meaning of academic writing by raising questions about student agency, authorship, trust, and academic integrity." Additionally, "GenAI's educational value is conditional and depends on alignment among tool capabilities, user capability, faculty judgment, assessment design, and institutional governance."

Reports effect sizes.

Research paradigm

Critical interpretivism / Constructivism

Author conclusions

The authors conclude that "GenAI's educational value is conditional and depends on alignment among tool capabilities, user capability, faculty judgment, assessment design, and institutional governance." They further recommend that "Future research should examine how GenAI affects learning development and responsible use across diverse learners, disciplines, and institutional contexts."

Risk of bias

Publication bias (only published studies included in review); Selection bias in literature identification (search strategy not detailed in abstract); Reporting bias (studies may selectively report favorable outcomes); Discipline and institutional context bias (generalizability concerns noted)

Open questions raised

  • Future research should examine how GenAI affects learning development and responsible use across diverse learners, disciplines, and institutional contexts. The review identifies governance and design challenges related to policy clarity, AI literacy, disclosure, overreliance, hallucinations, bias, equity, and responsible use.
  • How GenAI affects learning development across diverse learners
  • Responsible use patterns across different disciplines
  • Implementation across diverse institutional contexts
  • Long-term effects on student agency and authorship development
  • Effective governance and design strategies for institutional implementation
Data: not_statedCode: not_statedExtracted from: pdfAgreement 79%

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