What is Left for Us? Second Scholarship Against the Degradation of Research by AI

2026-07-04

AI Summary

This study argues that generative AI may undermine the very practices through which scholarly judgment and trust are cultivated, potentially stalling researchers’ intellectual development despite superficially optimized outputs. To counter this risk, the paper introduces the concept of “second-order scholarship,” which reconceives research as a living practice grounded in friction, communal engagement, and irreducibly human epistemic virtues. Drawing on philosophical reflection, educational theory, and the sociology of academia, the work identifies four foundational pillars—tacit knowledge, personal commitment, socialization, and deep reading—that delineate the boundaries of human–AI collaboration. By elucidating the inherent limitations of generative AI in authentic scholarly inquiry, the study offers a theoretical framework and ethical guidance for preserving research as a formative space for intellectual growth.

Abstract

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, 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 reappropriation 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.

Problem

• generative AI
• scholarly judgment
• academic trust
• research degradation
• intellectual formation

Innovation

• second scholarship
• tacit knowledge
• generative AI
• academic trust
• scholarly practice