Sep 2026· Journal of the Association for Information Science and Technology· 0 citations· 14 references
Abstract
The rapid adoption of generative AI has sparked debates about whether we are experiencing revolutionary disruption or evolutionary transformation of the information landscape. While headlines proclaim unprecedented changes, understanding AI's impact requires situating current developments within the evolution of how we have created, shared, and used information over centuries. We argue that generative AI represents not a radical break, but the latest interim layer in an evolving information landscape, progressing from pre‐digital systems through centralized computing, personal computing, the web, and social media. We outline the characteristics of each layer, including information infrastructures, information flows, and human roles. We discuss how layers have built upon predecessors while introducing novel challenges and opportunities: the web democratized publishing, social media enabled viral many‐to‐many networks, and generative AI now positions machines as co‐creators in hybrid intelligence systems. This means, rather than displacing earlier infrastructures, developments like generative AI add complexity to the information landscape, in which multiple layers coexist and interact. By recognizing both continuity and novelty in the evolution of our information infrastructure, we can more effectively navigate the generative AI era and ensure that technological advancement augments rather than replaces human agency, while preparing for the sociotechnical challenges it inevitably brings.
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
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
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