Abstract Debates on the cognitive impact of Generative AI often assume a homogeneous model of human cognition and focus on individual performance. AI’s epistemic authority in contemporary life raises concerns about the social conditions for democratic inquiry and human flourishing. Drawing on Dewey’s pragmatist conception of growth as a collective, pluralist practice and Jaeggi’s critique of forms of life, this paper argues that AI’s tendency toward cognitive homogenization represents a structural risk to democratic deliberation. A critical examination of recent empirical studies on AI and cognition reveals that their experimental designs systematically privilege Neurotypical frameworks, pathologizing alternative cognitive strategies – such as cognitive offloading – while rendering Neurodivergent ways of thinking invisible. Against this, the paper proposes a Disability Justice-informed model of AI design grounded in cross-neurotype solidarity and cognitive pluralism. This paper argues that such approaches overlook both cognitive plurality and the social conditions of human flourishing. From a neurodiversity perspective, Generative AI can function as cognitive scaffolding rather than cognitive substitution, particularly for individuals whose modes of thinking diverge from dominant norms, contributing to a more democratic epistemic practice.
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.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026