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AI evidence extraction

Sharpening the Pencil, Not Replacing the Hand: De‐Stigmatising AI Use in Research Writing

Aaron Opdyke · Learned Publishing · 2026

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

9/10
Relevance
1/4
Quality (LMQS)
I
Evidence
0
Citations
0.00
FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1002/leap.2055

Methodology & findings

Main result

The paper finds that "most researchers accept AI in writing, yet stigma around disclosure persists—revealing a gap between practice and transparency." The authors argue that responsible AI engagement rather than blanket avoidance better mitigates risks, and that disclosure frameworks should distinguish between surface editing and deeper co-production.

Reports effect sizes.

Research paradigm

Critical interpretivism / Social constructivism

Author conclusions

The authors conclude that "AI disclosure should be reframed from confession to scholarly norm, modelling accountability rather than signalling weakness" and that "responsible AI engagement, not blanket avoidance, best mitigates risks of hallucination, bias, and epistemic homogenisation."

Risk of bias

Potential author bias toward normalizing AI use; Limited empirical data presented to support claims about researcher acceptance rates; No systematic evidence synthesis provided

Open questions raised

  • The paper identifies a gap between practice and transparency in AI disclosure among researchers, suggesting need for clearer frameworks distinguishing different types of AI use in research writing.
  • The paper identifies the need for reframing AI disclosure norms in academia and the development of differentiated disclosure frameworks that distinguish between surface-level editing and deeper co-production uses of AI.
  • The paper identifies the need for clearer frameworks distinguishing different types of AI use in research writing and calls for moving beyond stigma toward evidence-based disclosure practices.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 83%

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