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Paper short abstract

We examine how genAI reshapes academic research promising efficiency yet also raising various risks (e.g., bias, hallucinations). Drawing from scenarios that extrapolate scientists’ today’s genAI practices into near-future research workflows we map future trade-offs and discuss implications.

Paper long abstract

Generative AI (genAI) is increasingly embedded in academic knowledge production. Many researchers expect gains in efficiency and research quality, yet genAI also raises ethical risks, including weak transparency, biased outputs, hallucinations, limited contextual understanding, and privacy concerns. With the political alignment of most Big Tech companies with the far-right US government under Donald Trump, ethical questions regarding the use of genAI for (impartial) knowledge generation are even more questionable.

To anticipate future frictions in the academic use of genAI for knowledge production, we conducted a first explorative workshop with eight scholars spanning different disciplines (computer science, sociology, psychology), cultural backgrounds (Germany, Russia, Brazil), and career stages (doctoral to professorial) in December 2024. Applying scenario writing (Kieslich et al., 2025), participants produced narratives that extrapolate today’s practices into near-future research workflows. Three themes stand out. First, genAI appears less as a remedy than as a symptom of an overburdened academic system: workload, funding competition, and publication pressure incentivize shortcuts and can foster mistrust. Second, scenarios highlight “efficiency at a cost”: synthetic data or AI-generated ‘participants’ may yield persuasive but ungrounded results and intensify misrepresentation of marginalized groups when model biases are reproduced. Third, participants raise value-laden trade-offs in which funding and peer review normalize uncritical AI use, disadvantaging researchers who resist.

At EASST conference, we plan to discuss the narratives in more detail, elaborate on future research perspectives based on our exploratory work and what, in our view, deserves particular attention when assessing the role of generative AI in academic research.