AI evidence extraction

A dancing bear, a colleague, or a sharpened toolbox? The cautious adoption of generative artificial intelligence technologies in digital humanities research

Rongqian Ma, Meredith Dedema, A Cox · Journal of the Association for Information Science and Technology · 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

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1002/asi.70066

Methodology & findings

Study design

Mixed methods study combining an international survey (n=76) and 15 in-depth semi-structured interviews.

Sample

N = 91, 2 groups

Main result

The study found divergent opinions within the DH community regarding GenAI adoption. The authors report that "while many scholars view GenAI as a means to enhance efficiency and support reskilling, others express concern about its impact on scholarly identity, intellectual labor, and disciplinary values." Additionally, "GenAI is being incrementally enrolled into DH research networks, reshaping relationships among human and nonhuman actors in ways that remain contested and actively negotiated."

Reports effect sizes.

Research paradigm

Mixed methods (qualitative-dominant with quantitative survey component); interpretivist/constructivist with actor-network theory orientation

Author conclusions

The authors conclude that "as one of the first empirical studies on this topic, this work provides an initial foundation for understanding GenAI's evolving role in DH scholarship and points toward avenues for future research."

Risk of bias

Potential selection bias (self-selected survey respondents), unclear sampling strategy for interview participants, geographic/disciplinary representation not specified; Selection bias: International survey respondents may be self-selected volunteers with particular attitudes toward GenAI (adoption-prone or skeptical scholars more likely to respond); Attrition/non-response bias: Response rate of survey not specified in abstract; Self-report bias: Reliance on scholar-reported perceptions and practices without behavioral validation; Representativeness: 76 survey respondents across international Digital Humanities community may not be fully representative of all DH scholars; Self-selection bias in survey respondents (those willing to participate may have different views on GenAI than non-respondents); Possible volunteer bias in interview participants; Limited geographic diversity information not specified in abstract

Open questions raised

The authors identify that this is among the first empirical studies on GenAI adoption in Digital Humanities and point toward avenues for future research to deepen understanding of GenAI's evolving role in DH scholarship
The authors identify this as "one of the first empirical studies on this topic" and note it "provides an initial foundation for understanding GenAI's evolving role in DH scholarship and points toward avenues for future research," suggesting the need for continued investigation of GenAI adoption trajectories, long-term impacts on disciplinary practices, and deeper exploration of the contested negotiations around GenAI's role in scholarly work.
The authors identify that their study is among "the first empirical studies on this topic" and that the work "points toward avenues for future research" on GenAI's role in DH scholarship, suggesting gaps remain in understanding this emerging phenomenon.

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