Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Publishing and Scholarly Communication
Abstract
This paper examines how generative AI may alter the production and validation of knowledge. It argues that while AI substantially lowers the cost of drafting, translating and polishing intellectual work, it does not appear to reduce the cost of validating claims to the same degree. As a result, the primary bottleneck in knowledge production may shift from access to validation. The paper distinguishes production from validation, expertise from credentials, and participation from recognition. Drawing on evidence from scholarly publishing, peer review, research funding and AI governance between 2023 and 2026, it reviews documented cases of increased participation, failures of verification and institutional responses. It proposes two contrasting scenarios: adaptive gatekeeping, which focuses on improving assessment of claims, methods and evidence, and defensive gatekeeping, which relies more heavily on credentials, affiliation or procedural barriers. The analysis treats AI systems as tools rather than autonomous knowers and argues that expertise remains necessary even in an environment where the production of polished text becomes inexpensive. The paper concludes that the future impact of generative AI on knowledge production will depend largely on how institutions manage the growing challenge of validation. Full discloure, this first draft was AI edited as noted. This version has been uploaded as a digital record.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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