Generative Digital Humanities

Matthew Kirschenbaum, Lauren Klein, Lisa Nakamura, and Fabian Offert, Editors

Deadline for 300-500 word abstracts: November 1, 2026

Part of the Debates in the Digital Humanities Series
A book series from the University of Minnesota Press
Matthew K. Gold and Lauren F. Klein, Series Editors

For most people, the release of ChatGPT in November 2022, and the wave of generative AI tools that it soon ushered in, represented something wholly new. But for those in the field of DH, the proliferation of large language models (LLMs) and genAI tools was not altogether unexpected. After all, many DHers had long made use of machine learning and natural language processing techniques in their research. When the first useful transformer models started to appear, some immediately began to wonder about the models’ broader implications for our understanding of culture. Others, meanwhile, had already trained their critical eye towards the ever-more-overt complicity of technology and power. With equal prescience, they were able to surface the intersections between capitalism, illiberalism, and white supremacy that had aided the rise of Big Tech, not to mention the sexism, racism, ableism, and other biases embedded in the technologies it sells.

Within the university, a parallel tension began to emerge: those whose work sat at the intersection between the humanities and computation found themselves with a seat at the table of institutional AI task forces and policy discussions. For those who had long been critical of the close proximity of DH to the neoliberal university, this participation seemed to offer unassailable evidence of the complicity of DH with the very worst of technocapitalist agendas. Within this contested terrain, one certainty has emerged: debates in and about the digital humanities–as conducted through this book series as well as other venues–have emerged as crucial lines of questioning as we work to understand the range of implications of AI as a technology and as a construct.

Starting from the premise that AI is both a technology and a sociopolitical construct–that is, that it can be used to advance certain forms of humanistic knowledge at the same time that it actively undermines other forms of humanistic knowledge, cultural expression, and creative work–this volume asks: how can digital humanities intervene? After all, it is exactly the friction produced by training AI models on, and applying AI models to, cultural data that informs the most salient critiques of AI–the critiques that, we believe, must feed back into technical practice to produce actual change. This volume will provide a platform for this crucial work.

By facilitating a much-needed exchange between the critical-computational research currently taking place in the humanities and more technical fields, we seek to contribute a set of grounded interventions that point to as-yet-considered possibilities as well as to potential (and actual) harms. More than that, we seek to model the value of interdisciplinary dialogue that is undertaken from a place of mutual respect, a desire for shared understanding, and a goal of transforming the current status quo. If the advent of AI has exposed what many in the humanities have long known to be true–that is to say, the inextricability of technology, politics, and power–then this volume seeks to create a space for what comes next: how we continue to build solidarity across disciplines, advance our collective knowledge, and together envision and then work to enact a world in which all of us can thrive.

With these goals in mind, we invite essays including but not limited to the following areas:

AI and/as DH? How has generative AI informed and augmented–or, alternately, undermined–current or historical DH practices? What novel considerations and complications does it introduce? For example, has “vibe coding” finally resolved the original schism of the discipline (“do I have to learn how to code?”) or is it now more important than ever to develop slow, minimal, and deliberate technical (or analog) practices? Likewise, are there new uses, or applications, or audiences, for certain long-standing DH projects, initiatives, and infrastructures, in the current context of AI? Or are there ways in which such projects are now obsolesced? More generally, how can DH scholarship inform the design, development, or deployment of AI technologies, or the other way around?
“Generative” practices: While early AI and ML was mostly-task specific, the recent wave is characterized by the generation of textual and visual material from instructions. What does this mean for DH, which has most often adopted an analytical posture and with it the comforts of distance or disinterest? How do we assure archival integrity if our tools are probabilistic to their core? And how do we merge the cultural record with synthetic cultural data–that is, representations without referents? How do we, in our role as stewards of data and archives, safeguard against that likely eventuality? Can fabrication/fabulation (even hallucination) ever be critically salient? If so, where exactly lies the threshold between merely generative and generatively speculative practices?
Disciplinary politics: DH has long been accused of colluding with neoliberal agendas (and tools), but the deep, causal, and intentional entanglement of artificial intelligence and authoritarianism requires us to reevaluate the field’s relationship to power and capital. How can we make sure that an AI-informed DH does not further amplify AI’s immiserating social and economic effects? Can application and critique be conjoined to form a critical technical practice? How complicit is DH in the rise of Big Tech, and are there limits to how interventionist it can or should be? What would spaces of refusal or abolition within DH look like, or outside of DH? How does critical work in fields like ethnic studies, gender studies, and queer theory, among others model forms of refusal and fugitive study in ways that enrich and inform abolitionist thinking?
DH and the new AI “literacies”: Like so much of current educational practice, “literacy” as an outcome has become a vector of corporate capture. Is DH at all positioned to reassert aspects of that term, its import and its criticality? Put otherwise: as the status of both technical and creative expression are together being renegotiated by AI, are there versions of AI literacy that advance the goals of greater understanding and informed use, or has the idea of “literacy” become yet another means of devaluing domain expertise? What should we be teaching students about AI and how should AI feature in our teaching, if at all?
New mappings of local and global DH: Debates about US centrism and historical patterns of colonialism (and the exclusions they bring about) are not new to DH. How can this history and depth of engagement advance conversations about AI’s current global order or “empires”? To what extent can DH practices developed in the majority world be brought to bear on the current landscape of AI? What of the field’s longstanding engagement with questions of labor? Can, and if so, how can the perspective gained from DH’s reflexiveness about its own labor practices be applied to the exploitative labor conditions that characterize so much of AI work? Can DH theorizations of hierarchy and credit be rendered productive for a more extensive critique of AI’s renegotiation of class and labor? How can Big Tech’s hijacking of energy infrastructure and its exploitation of the environment be theorized through and within DH?
Institutional interventions: In the US, the university has found itself in the crosshairs of the current political regime, with attacks on academic freedom and the elimination of federal offices and funding programs–including the NEH Office of Digital Humanities–in the national headlines. How does the massive institutional uptake of AI enable, extend, complicate, or contravene these trends? Are there ways to use the paradoxically increased visibility of the humanities (as a target or as a “durable skill”) as a form of advocacy? For what and on behalf of whom? Is there additional opportunity as private funders look to the humanities alternately as a source of untapped data or research questions or as a savior for humanity writ large? Are there lessons to be learned, or strategies adapted, from the role that DH has historically played as a disciplinary intermediary?

In addressing these or other topics, submissions should take an argumentative stance, advocating clearly and explicitly from a particular point of view. DDH does not publish case studies. Scholars and practitioners from across the disciplines (regardless of rank, position, or institutional affiliation) are invited to submit abstracts of 300-500 words, along with a 2-page CV for each contributor, by November 1st, 2026, to one of the following tracks:

Essay track. 4000-6000 word chapters that clearly intervene into one or more of the debates outlined above.
Interview track. 2000-4000 words interviews with, or discussions between, scholars and practitioners in DH and other fields that probe one or more of the debates outlined above. Proposals should identify the participants and their engagement with some or all of the themes described above.
Project track. 500-1000 word snapshots of projects that can only be done with–or without–AI. This track is designed to showcase concrete examples of how DH projects (in active development or from the past) intervene into the debates above. Abstracts should address both the project’s discursive contribution and explain which models are used or avoided and why. We also encourage the inclusion of infrastructural parameters like energy costs, compute used, labor and expertise (especially as it relates to data curation), etc.
Experimental track. We welcome submissions whose format and genre conventions do not fit any of the tracks above. Artwork for example, or textwork; source code, prompts, dumps, and grabs; or speculative data sets or data sheets. Abstracts on this track should be sure to delineate both the particulars of the contribution and its engagement with one or more of the debate points mentioned above, as well as the specific format the contribution will take.

Please submit abstracts through this form.

The provisional publication timeline is as follows:

Deadline for proposals: November 1, 2026
Decisions on accepted proposals: December 1, 2026
Drafts due: March 1, 2027
Peer-to-peer review: April/May 2027
Editorial feedback: Summer 2027
Revised submissions due: Summer/Fall 2027
Final submission of volume to Press: Spring 2028
Publication date: Spring 2029