2025/08/07 by Henrik Skaug Sætra · 1 voice · 1 citation
Decision Sciences · Social Sciences · #Ethics and Social Impacts of AI #Innovation, Sustainability, Human-Machine Systems #Scientific Computing and Data Management
paper · pdf · doi:10.1007/s00146-025-02557-7
openalex publication_date 2025/08/07 · openalex created_date 2025/08/16 · openalex updated_date 2026/07/29
Abstract Generative Artificial Intelligence (Gen AI) now allows for the seeming automation of most if not all steps in the scientific research lifecycle, giving rise to what I refer to as the Research Automaton – the production of science-like output with minimal meaningful human engagement. This development is often framed through a techno-solutionist lens, promising efficiency gains by treating the traditional, often strenuous, research process as a problem to be solved. This paper challenges that perspective, arguing that the intrinsic value of science lies in this very process rather than solely in the product . Uncritically embracing automation thus entails eroding the formative experiences crucial for researcher development, particularly for early-career researchers, leading to potential skill atrophy and undermining the long-term innovative capacity of science. Drawing on both normative arguments about science as a vocation and pragmatic concerns about preserving essential cognitive and critical skills, I advocate for resisting the Research Automaton, while acknowledging the potential for AI to augment human capabilities when used judiciously within a hybrid cognitive constellation. I conclude by outlining practical implications for researchers, supervisors, institutions, policymakers, and publishers in navigating the integration of Gen AI in research.