mlsanityData to 7 Oct 2026

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

arXiv 2607.04049 ↗First posted 4 Jul 2026cs.AIcited by 0

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Abstract (arXiv)

We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed. As constitutive conditions for the production and validation of knowledge, these practices cannot be reduced to the outputs of research, which is what AI tools so effectively simulate. When scholars delegate central tasks of inquiry to Large Language Models (LLMs), they may stop engaging in these practices and lose the formation they provide: the output may improve while the researcher 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 friction, 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. These enact what we call second scholarship, i.e., the reappropriation of scholarly craft, chosen out of a critical experience of what AI tools can and cannot do. Researchers who practise it may delegate tasks to these tools, provided that their own judgement in the delegated competence is already formed, that they can answer for the output, and that they keep exercising the competence themselves. What cannot and should not be delegated becomes what research communities must value and answer for. This is what is left for us.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 98 references that are in the map. First posted on arXiv 4 Jul 2026.

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14 of 98 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  4. 2024OASIS: Open Agent Social Interaction Simulations with One Million Agentscited by 127
  5. 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 103
  6. 2024Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performancecited by 58
  7. 2025Generalization Bias in Large Language Model Summarization of Scientific Researchcited by 30
  8. 2026Scientific production in the era of Large Language Modelscited by 30
  9. 2025The simulation of judgment in LLMscited by 22
  10. 2026The Last Human-Written Paper: Agent-Native Research Artifactscited by 12
  11. 2025A Replica for our Democracies? On Using Digital Twins to Enhance Deliberative Democracycited by 3
  12. 2025AI and the Future of Academic Peer Reviewcited by 3
2 more
  1. 2026Large Language Models and Scientific Discourse: Where's the Intelligence?cited by 2
  2. 2026Fake Plastic Voters: When Political Parties Can Use AI-Simulated Focus Groupscited by 1

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