AI-supported deliberation platforms increasingly support large-scale participation in public policymaking. Yet their design reflects strong rationalistic and emotion reductionist bias: while they structure arguments, cluster opinions, visualize disagreement or organize discussions, they largely ignore the emotional dynamics through which participants interpret claims, react to disagreement and sustain engagement. This blind spot is particularly problematic in discussions surrounding wicked public issues, where emotions are not peripheral but integral to how actors evaluate arguments and orient themselves toward collective consensus-oriented decisions. At the same time, recent advances in Generative Artificial Intelligence (GenAI) introduce new possibilities for interacting with emotional signals in digital communication. Beyond content processing, GenAI systems can detect, interpret, and generate context-sensitive emotional expressions; this opens the possibility of GenAI-mediated emotional support in deliberative environments. This study explores how such systems could be designed. Following an echeloned Design Science Research approach, the paper focuses on the initial stages of a broader design project aimed at developing a GenAI mediator for consensus-oriented online deliberation. The problem space is examined through an analysis of 3 existing deliberation platforms and a semisystematic literature review of 25 papers on emotional dynamics in consensus-oriented discussions. Using a three-step thematic synthesis, the study derives design knowledge, articulated through design requirements, for emotion-aware GenAI mediation. The results of this study consist of two outcomes. First, the analysis formulates a problem statement that identifies a gap in current deliberation platforms: while they organize informational exchanges, emotional dynamics remain unmodeled. Second, the study derives six design requirements that define the design space a solution must satisfy: emotional awareness, emotional regulation, emotional inclusive design, emotional stability, emotional conflict transformation, and emotional articulation. These findings contribute to an initial body of design knowledge that bridges research on emotions in deliberation with emerging capabilities of GenAI; they lay the groundwork for designing emotion-aware GenAI-mediated deliberations.
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
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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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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