The conference will be held at AIAS, at

Aarhus University
Høegh-Guldbergs Gade 6B
DK-8000 Aarhus C

The conference fee (DKK 500) covers food on both days, but if you wish to attend the conference dinner on Monday evening there is an additional payment (DKK 500). Registration are binding, and deadline for registration is 8 March.

For your convenience, we have compiled some practical travel information:

Accommodations in Aarhus in three different price ranges:

• CABINN
• WakeUp Aarhus
• Scandic Aarhus City

Transportation to Denmark:

• Billund Lufthavn. (1h15-1h30 by bus to Aarhus city center)
• Aarhus Airport. (45 min by bus)

Transportation in Denmark

• Train: DSB.
• Busses from the airports to Aarhus: Midttrafik

Generative AI is not just a technical breakthrough – it’s reshaping how we create, communicate, and understand our cultural heritage.

By producing vast amounts of text, art, and other expressive forms, these technologies challenge assumptions about authorship, authenticity, expertise, and creativity itself. They also raise urgent questions about the cognitive, emotional, and social mechanisms at play when individuals co-create with AI. These technologies raise urgent questions about transparency and power, while their potential to augment or replace human skills calls for a re‑examination of craft, education, and cultural values.

The propagation of generative AI in literature re-actualizes discussions of authorship from twentieth-century, post-structuralist debates (Barthes 2016; Foucault 2003). The increasing difficulty of distinguishing AI-generated texts from human-written ones casts discussions of authorship in a new light (Bajohr 2023). Consider the rapid decline in the amount of self-published books on Amazon that list ChatGPT as an author (Hongisto 2025). It is likely not the case that the actual use of ChatGPT to produce self-published books is decreasing with the same rate.

Instead, what we are witnessing is a decreased interest in the notion that computers can produce cohesive text that is recognizable as being of a literary kind. It is simply not special or even unexpected that computers have nontrivial impact on processes of writing. However, we still lack robust psychological models to explain how writers adapt cognitively to AI collaboration – including effects on problem-solving, goal-setting, and self-efficacy.

At this conference, we invite scholars and thinkers from a variety of fields such as comparative literature, critical AI studies, critical data studies, cognitive science, psychology, human-computer interaction, and digital and computational humanities to take on the pressing and multi-layered question of how AI is transforming creative practice, cultural transmission, and human experience.

Generative AI troubles the minor but longstanding practice of producing literary text with computers. Since the advent of the digital computer, people have used it to produce poems, prose, and conceptual texts (Hayles 2004; Bertram and Montfort 2024).

However, in a recent call from the literary journal Michigan Quarterly Review, for an issue focusing on computer-generated text, the editors explicitly discouraged submissions based on generative AI without significant conceptual reworking (Michigan Quarterly Review 2025). In this sense, it is not just the case that traditional notions of authorship are being destabilized; so too are the practices and self-perceptions of experimental writers. From a psychological perspective, generative AI may not only influence external outputs but also shape writers' internal states: their motivation, metacognitive strategies, and identity as creators. Integrating theories from creativity research (Amabile, 1996; Csikszentmihalyi, 1996), self-determination theory (Deci & Ryan, 1985), and social cognition (Markus & Nurius, 1986) can provide deeper insight into how human-AI co-authorship unfolds.

Furthermore, recent empirical work highlights the metacognitive demands of using generative AI systems, suggesting a need to examine how these tools influence planning, reflection, and confidence during the writing process (Tankelevitch et al., 2024). Finally, emerging studies show how writers navigate shifts in identity and ownership when their work is integrated into or influenced by large language models (Gero et al., 2025). It is tempting to assert that generative AI is the latest and perhaps final fulfilment of the technologizing of the word: text has always been technical, and so has the book-bound codex (Ong 2002; Portela 2013). Avant-garde movements such as the OuLiPo established literary manifestations of technical systems already in the second half of the previous century.

Generative AI is, however, not only related to an abstract idea of authorship but is instead a concrete, material, and infrastructural apparatus with nontrivial power dynamics. As such, the question of literature in the context of generative AI often relates to discussions of data ethics (Rowberry 2025), interpretability (Dobson 2023), bias (Gillespie 2024), and environmental impact (Crawford 2021). Such questions fold into discussions of creativity (Doshi and Hauser 2024), critical thinking (Lee et al. 2025), and notions of quality (Porter and Machery 2024).

Ultimately, the destabilization of both traditional and experimental authorship by generative AI may converge to solidify in new literary practices and forms that may then, in turn, significantly impact the future development of AI (Rettberg and Rettberg 2025).

Organized by TEXT (Janet Rafner, Mads Rosendahl Thomsen and Anna Katrine Mathiassen) and HAIC-III (Søren Pold and Malthe Stavning Erslev)

References

Amabile, T. M. (1996). Creativity in context: Update to the social psychology of creativity. Westview Press.

Bajohr, Hannes. 2023. “Artificial and Post-Artificial Texts: On Machine Learning and the Reading Expectations Towards Literary and Non-Literary Writing.” Basel Media Culture and Cultural Techniques Working Papers, no. 007 (March): 1–31. https://doi.org/10.12685/bmcct.2023.007.

Barthes, Roland. 2016. “The Death of the Author.” In Readings in the Theory of Religion, 141–45. Routledge.

Bertram, Lillian-Yvonne, and Nick Montfort. 2024. Output: An Anthology of Computer-Generated Text, 1953–2023. MIT Press.

Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.

Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention.

Harper Perennial.Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.

Dobson, James E. 2023. “On Reading and Interpreting Black Box Deep Neural Networks.” International Journal of Digital Humanities 5 (2-3): 431–49. https://doi.org/10.1007/s42803-023-00075-w.

Doshi, Anil R., and Oliver P. Hauser. 2024. “Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content.” Science Advances 10 (28): eadn5290. https://doi.org/10.1126/sciadv.adn5290.

Foucault, Michel. 2003. “What Is an Author?” In Reading Architectural History, 71–81. Routledge.

Gero, Katy Ilonka, Meera Desai, Carly Schnitzler, Nayun Eom, Jack Cushman, and Elena L. Glassman. 2025. “Creative Writers’ Attitudes on Writing as Training Data for Large Language Models.” In CHI Conference on Human Factors in Computing Systems (CHI ’25), April 26–May 01, 2025, Yokohama, Japan, 1–16. ACM. https://doi.org/10.1145/37065983713287.

Gillespie, Tarleton. 2024. “Generative AI and the Politics of Visibility.” Big Data & Society 11 (2): 20539517241252131. https://doi.org/10.1177/20539517241252131.

Hayles, N. Katherine. 2004. “Print Is Flat, Code Is Deep: The Importance of Media-Specific Analysis.” Poetics Today 25 (1): 67–90.

Hongisto, Tuuli. 2025. “Advertising with AI – On the Presentation of Authorship of ChatGPT-Generated Books.” Electronic Book Review, March. https://electronicbookreview.com/essay/advertising-with-ai-on-the-presentation-of-authorship-of-chatgpt-generated-books/.

Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. 2025. “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.” CHI Conference on Human Factors in Computing Systems (CHI ’25). https://doi.org/101145/3706598.3713778.

Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954–969.

Michigan Quarterly Review. 2025. “Special Issue on Computer-Generated Text.” June 2, 2025. https://mqr.submittable.com/submit/318008/a-special-issue-on-computer-generated-text.

Ong, Walter J. 2002. Orality and Literacy: The Technologizing of the Word. Routledge.

Portela, Manuel. 2013. Scripting Reading Motions: The Codex and the Computer as Self-Reflexive Machines. MIT Press.

Porter, Brian, and Edouard Machery. 2024. “AI-Generated Poetry Is Indistinguishable from Human-Written Poetry and Is Rated More Favorably.” Scientific Reports 14 (1): 26133. https://doi.org/10.1038/s41598-024-76900-1.

Rettberg, Scott, and Jill Walker Rettberg. 2025. “Algorithmic Narrativity: Literary Experiments That Drive Technology.” Dialogues on Digital Society 1 (1): 37–40. https://doi.org/10.1177/29768640241255848.

Rowberry, Simon. 2025. “The value of books in the age of generative AI training data.” Convergence 0 (0): 1-16. https://doi.org/10.1177/13548565251358020

Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. In CHI Conference on Human Factors in Computing Systems (CHI ’24). https://doi.org/10.1145/3613904.3642902

The following speakers are confirmed:

• Roger Beaty, Pennsylvania State University
• Nina Begus, University of California, Berkeley
• Kyle Booten, University of Connecticut
• Katy Gero, University of Sydney
• Karin Kukkonen, University of Oslo

Roger Beaty

Roger E. Beaty is Associate Professor of Psychology at Pennsylvania State University and directs the Cognitive Neuroscience of Creativity Lab. He earned his PhD at UNC Greensboro and held a postdoc at Harvard. His research employs brain imaging (fMRI), network analysis, and neuromodulation to study creative thinking across domains, investigating neural dynamics underlying metaphor production, scientific ideation, and creative expression. He also develops open-access tools for creativity assessment using computational and NLP methods.

Nina Beguš

Nina Beguš is an Assistant Professional Researcher at UC Berkeley’s Center for Science, Technology, Medicine & Society (CSTMS), and was CSTMS Postdoctoral Scholar from July 2022 to June 2024. She earned her PhD in Comparative Literature from Harvard University and is the creator of the “Artificial Humanities” framework, exploring the contributions of fiction and the humanities to AI design. She also serves as a researcher in residence at Berkeley Lab (Berggruen Institute) and founded InterpretAI.

Kyle Booten

Kyle Booten is Assistant Professor of English at the University of Connecticut, Storrs. He earned a PhD in Education (Language, Literacy, & Culture) at UC Berkeley and was previously a postdoctoral fellow at Dartmouth’s Neukom Institute for Computational Science. His creative work—computer‑mediated and generated poetry - has appeared in venues such as Boston Review, Lana Turner, and Fence. His scholarly contributions address algorithmic systems, digital quotation culture, and computational poetics. 

Katy Ilonka Gero

Katy Ilonka Gero is a human‑computer interaction researcher focused on creativity, writing technologies, and the ethics of AI. A PhD graduate from Columbia University, she has held fellowships at Harvard University and the Library Innovation Lab and is currently a Lecturer in Computer Science at the University of Sydney - joining formally in July 2025. Her work examines how writers engage with language models, creative feedback, community‑driven AI, and the implications of model training practices. She is also a poet and essayist, with her first poetry collection The Anxiety of Conception slated for 2025. 

Karin Kukkonen

Karin Kukkonen is Professor of Comparative Literature at the University of Oslo, specializing in cognitive poetics, cognitive narratology, and the history of the novel. She is the author of A Prehistory of Cognitive Poetics: Neoclassicism and the Novel  (2017) and Probability Designs: Literature and Predictive Processing (2020), and currently leads the interdisciplinary research initiative “Literature, Cognition and Emotions” (2019–2023).