We argue that generative AI can degrade research by eroding the very
practices through which scholarly judgement is formed and academic trust is built. As
constitutive conditions for the production and validation of knowledge, these practices
cannot be reduced to the final outputs of research, which is what AI so effectively
simulate. Accordingly, when researchers delegate central tasks of inquiry to systems
like Large Language Models (LLMs), they may stop enacting these practices and, with
them,…
Read moreWe argue that generative AI can degrade research by eroding the very
practices through which scholarly judgement is formed and academic trust is built. As
constitutive conditions for the production and validation of knowledge, these practices
cannot be reduced to the final outputs of research, which is what AI so effectively
simulate. Accordingly, when researchers delegate central tasks of inquiry to systems
like Large Language Models (LLMs), they may stop enacting these practices and, with
them, lose access to the formation they provide. An individual research output
generated by AI may even appear improved but the researcher behind it fails to
develop. Against this risk, merely keeping humans "in the loop" as prompters or
quality-checkers of AI outputs is insufficient to preserve research as a site of intellectual
formation. What is needed instead is a renewed commitment to research as a lived
practice in which judgement is formed gradually, often through frictions, and
participation in a scholarly community. We defend it because it rests on four sources
and warrants of research that cannot be automated: tacit knowledge, personal
commitment, socialisation, and deep reading. This practice enacts what we call ‘second
scholarship’, by which we understand the re-appropriation of scholarly craft, chosen
out of a critical experience of what generative AI can and cannot do. What cannot and
should not be delegated becomes what research communities must value and answer
for. This is what is left for us.
