“But what is the alternative?!” - The impact of generative AI on academic knowledge production in times of science under pressure

Generative AI (genAI) is increasingly influencing academic knowledge production. While estimates vary on GenAI’s current use in academic research (Desaire et al., 2024; Strzelecki, 2025), rapid technological advancements (e.g., OpenAI’s deep research) and growing adoption suggest it will significantly shape the field. To this end, surveys among researchers (Al-Zahrani, 2024; Andersen et al., 2025) or comments of scholars (Huang & Tan, 2023) have voiced optimism regarding the role of genAI for academic research, for instance in increasing efficiency or improving the quality of research. Nevertheless, the use of genAI in research also raises a number of ethical challenges (Breuer, 2023; Selwyn, 2022). One key issue is transparency. Several high-impact publications have been flagged for including AI-generated content without disclosing its use, highlighting a lack of clear standards for acknowledging generative AI in scholarly work (Strzelecki, 2025). Also, genAI systems perpetuate societal biases (Bender et al., 2021), providing inaccurate information, lacking contextual understanding, or raising privacy concerns (Slattery et al., 2024; Zeng et al., 2024). With the political alignment of 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. As generative AI tools become more embedded in research workflows, the academic community must urgently address these issues and develop robust guidelines for transparent and responsible AI use. Therefore, it is necessary to foreground a prospective vision: how genAI could influence academic work in the near future? What consequences can be drawn from future scenarios to bolster responsible use?