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

Rongqian Ma et al.

Journal of the Association for Information Science and Technology (JASIST)2026https://doi.org/10.1002/asi.70066article
AJG 3
Weight
0.50

What the paper says

The emergence of generative artificial intelligence (GenAI) is reshaping the research landscape and carries significant implications for Digital Humanities (DH), a field long intertwined with computational methods and technologies. This study examines how DH scholars are adopting and critically evaluating GenAI in their research. Drawing on an international survey of 76 respondents and 15 in‐depth interviews, we investigate scholars' motivations for using GenAI tools, the specific practices through which they integrate these tools into their research, and their perceptions of the benefits, risks, and challenges associated with GenAI. Our findings reveal divergent opinions and imaginaries within the DH community: 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. Situated within the history of DH and analyzed through the lens of actor‐network theory, the results suggest that GenAI is being incrementally enrolled into DH research networks, reshaping relationships among human and nonhuman actors in ways that remain contested and actively negotiated. 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.

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https://doi.org/https://doi.org/10.1002/asi.70066

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@article{rongqian2026,
  title        = {{A dancing bear, a colleague, or a sharpened toolbox? The cautious adoption of generative artificial intelligence technologies in digital humanities research}},
  author       = {Rongqian Ma et al.},
  journal      = {Journal of the Association for Information Science and Technology (JASIST)},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/asi.70066},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.