arXiv:2607.04049v1 Announce Type: new 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
The paper is published as generative AI becomes ubiquitous, prompting reflection on its profound impact on established academic practices and knowledge creation.
This challenges the fundamental processes of research and scholarly judgment, crucial for maintaining academic integrity and the quality of knowledge production.
The perceived validity and originality of research outputs are now under scrutiny, necessitating new frameworks for academic trust and the evaluation of AI's role in inquiry.
- · Critics of generative AI in academia
- · Developers of AI ethics frameworks
- · Institutions prioritizing human-centric research
- · Researchers over-reliant on generative AI
- · Academic fields with poor quality controls
- · Publishers unable to detect AI-generated content
Increased debate within academia regarding the appropriate use and limitations of AI in research.
Development of new academic standards and ethical guidelines to address the challenges posed by generative AI.
A potential bifurcation in scholarly output, distinguishing between human-mediated and AI-assisted research with varying levels of trust and credibility.
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Read at arXiv cs.AI