Sep 2026· International Journal of Human-Computer Interaction· pp. 1-24
Abstract
Generative artificial intelligence (GenAI) is reshaping creative work, yet research focuses mainly on digital creation, with limited attention to embodied practices grounded in material interaction and physical making. Taking craft as an entry point, we examine GenAI in craft through a two-phase qualitative study with 28 practitioners across domains. We find a partial misalignment between GenAI’s result-oriented paradigm and craft’s process logic. GenAI tends to respond through instant generation and finished-state outputs, hindering temporal incubation, in which ideas and meaning emerge through repeated making, and imperfection transformation, in which errors and material constraints become creative opportunities. Craft practitioners do not simply accept or reject GenAI, but negotiate its role through selective adoption and boundary-setting. We propose a Human–GenAI Co-Creation Framework for Embodied Craft Practice, characterizing how human judgment, material resistance, and GenAI interact in making. This extends GenAI–creativity discourse beyond digital contexts and informs design of systems supporting embodied creation.
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.
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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.
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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
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026