AI evidence extraction

Empirical studies of writing and generative AI: Introduction to the special issue

Chris Anson, Kirsti Cole · Journal of Writing Research · 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)

Evidence

Citations

7.33

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17239/jowr-2026.17.03.01

Methodology & findings

Study design

This is an editorial introduction to a special issue presenting a collection of empirical studies using methodological pluralism, including: experimental comparisons, mixed-methods intervention designs, corpus-based analyses, computational linguistic techniques, and qualitative interpretive approaches..

Main result

The special issue demonstrates that "the studies demonstrate the necessity and value of methodological pluralism for investigating a complex, rapidly evolving phenomenon" by collectively examining seven empirical studies using diverse approaches including "experimental comparisons, mixed-methods intervention designs, corpus-based analyses, computational linguistic techniques, and qualitative interpretive approaches." These methodologies enable investigations into comparisons of AI and human performance, how writers of different ages and expertise engage AI tools, how assessment systems respond to AI-generated prose, and how readers interpret texts with ambiguous authorship.

Reports effect sizes.

Research paradigm

Mixed methods (pluralistic)

Author conclusions

The authors conclude that "the studies demonstrate the necessity and value of methodological pluralism for investigating a complex, rapidly evolving phenomenon," and that together "these methods enable lines of inquiry that no single approach could sustain: comparisons of AI and human performance in professional writing tasks; analyses of how writers at different ages and levels of expertise engage AI tools; examinations of how assessment systems register and respond to AI-generated prose; and investigations of how human readers interpret texts with ambiguous authorship."

Limitations

The paper indicates that examining generative AI "by foregrounding both the affordances and limitations of different methodological traditions" is necessary, though it does not explicitly state limitations of the review itself but rather emphasizes that no single methodological approach could sustain all lines of inquiry.

Open questions raised

The special issue implicitly identifies research gaps by demonstrating that multiple complementary methodological approaches are needed to investigate writing and generative AI, suggesting that single-method approaches are insufficient for understanding this complex phenomenon.
The special issue implicitly identifies gaps by examining: comparisons of AI and human performance in professional writing tasks, analysis of how writers at different ages and expertise levels engage with AI tools, examination of how assessment systems respond to AI-generated prose, and investigation of how human readers interpret texts with ambiguous authorship.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 76%

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