Plenary Lectures

We are delighted to invite you to two special sessions as part of our Summer School social programme. 

• Tuesday, 11th of August | Aula Polivalente, Campus EST | 17:00 - 20:00
• Tuesday, 18th of August | Aula Magna, Campus WEST | 17:00 - 20:00

Publishing in the GenAI age

Outline and background

Generative Artificial Intelligence tools are increasingly used in the research and academic publishing process (Lepori et al., 2026). Recent analyses based on language use suggested between one-quarter and one-third of scholarly papers in some fields are already written with at least some GenAI assistance (Kobak et al., 2024; Liang et al., 2025). Although these estimates are necessarily indirect and operate at the corpus level, they indicate that GenAI is no longer a marginal or experimental tool, but is becoming part of the current infrastructure of scholarly production (Kousha, 2024; Siler, 2026). There is also some evidence that GenAI is also increasingly used for reviewing papers (Gartenberg et al., 2026), despite the fact that this is forbidden by journals and considered as a bad practice by most academics (Andersen et al., 2025).

The implications for the publishing of GenAI adoption are currently poorly understood. At the level of scholars submitting papers, some preliminary evidence is emerging that GenAI increases the efficiency of paper production, and improves linguistic quality (Alsudais, 2025), but papers largely relying on Generative AI are less able to engage a targeted audience and contain more standardized moves and argumentation structures (Fredrick & Craven, 2025). It has been suggested that GenAI use is trading quality for quantity (Gartenberg et al., 2026) and there is evidence that acceptance rates of paper using GenAI are much lower than human-produced papers at least in top-journals. However, many evaluation systems prime researchers based on the volume of (mostly incremental) publications rather than on the most innovative works (Boudreau et al., 2016).

This context is likely to significantly affect young scholars and their publishing process and will likely face them with difficult choices and trade-offs. In the context of a rapidly increasing number of (GenAI-assisted) paper submissions, they will need to strike a balance between enhancing their productivity and being still able to produce innovative work that is also able to engage with their audience; in a context where a share of the reviews will be, at least, assisted by GenAI with an impact difficult to predict on evaluation outcomes. This might require a combination between innovative use of GenAI for some tasks and relying even more than in the past on established scientific institutions, like reliance on scientific networks, direct engagement with peers and crafting some core argumentative elements by hand.

While it is too early to provide clear guidance, the goal of these two plenaries will be to:

• Provide information based on emerging evidence.
• Share the experiences of instructors and of the school participants.
• Devise robust strategies for young scholars to manage the publishing process taking into account GenAI’s impact on the publishing and reviewing process.

Programme 11th August

• Moderation: tbc
• Introduction: the impact of GenAI on the publishing process. What we know and some directions for coping with it (10’)
  Tine Köhler, University of Melbourne
• Feedback and impulse statements by instructors (2 x 5’)
  Rudi Palmieri, University of Liverpool
  Giovanni Colavizza, University of Copenhagen
• Experience reports by two summer school participants (2 x 5’)
• Open discussion
• Networking Apéro

Programme 18th August

• Moderation: tbc
• Introduction: the impact of GenAI on the publishing process. What we know and some directions for coping with it (10’)
  Marco Steenbergen, University of Zurich
• Feedback and impulse statements by instructors (2 x 5’)
  Derek Beach, University of Aarhus
  Thomas Hills, University of Warwick
  Christina Silver, University of Surrey
• Experience reports by two summer school participants (2 x 5’)
• Open discussion

• Networking Apéro (sponsored by DemoSCOPE AG - Part of Norstat Group)

   

Selected references

Alsudais, A. (2025). Exploring the change in scientific readability following the release of ChatGPT. Journal of Informetrics, 19(3), 101679. 

Andersen, J. P., Degn, L., Fishberg, R., Graversen, E. K., Horbach, S. P., Schmidt, E. K., Schneider, J. W., & Sørensen, M. P. (2025). Generative Artificial Intelligence (GenAI) in the research process–A survey of researchers’ practices and perceptions. Technology in Society, 81, 102813. 

Boudreau, K. J., Guinan, E. C., Lakhani, K. R., & Riedl, C. (2016). Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Science, 62(10).

Fredrick, D., & Craven, L. (2025). Lexical diversity, syntactic complexity, and readability: A corpus-based analysis of ChatGPT and L2 student essays. Paper presented at the Frontiers in Education, , 10 1616935. .

Gartenberg, C., Hasan, S., Murray, A., & Pierce, L. (2026). More versus better: Artificial intelligence, incentives, and the emerging crisis in peer review.Organization Science, 37(3).

Kobak, D., González-Márquez, R., Horvát, E., & Lause, J. (2024). Delving into ChatGPT usage in academic writing through excess vocabulary. arXiv:2406.07016, . https://doi.org/10.48550/arXiv.2406.07016.

Kousha, K. (2024). How is ChatGPT acknowledged in academic publications? Scientometrics, 129(12).

Lepori, B., Andersen, J. P., & Donnay, K. (2026). Opinion paper: generative AI and the future of scientometrics. Scientometrics, .

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., & Huang, Z. (2025). Quantifying large language model usage in scientific papers. Nature Human Behaviour, .

Siler, K. (2026). The diffusion of large language models in published academic articles. Proceedings of the National Academy of Sciences, 123(22).