Advances in Generative AI for Design and Collaboration

The field of generative AI is rapidly advancing, with significant developments in its application to design and collaboration. Researchers are exploring the potential of generative AI to support early-stage client-designer collaboration, with a focus on improving mutual understanding and reducing misunderstandings. Another area of research is the impact of generative AI on social media, including its effects on user behavior and experience.

Noteworthy papers in this area include: The paper 'If we misunderstand the client, we misspend 100 hours' presents a three-phase study investigating how digital systems can support requirements elicitation in professional design practice. The paper 'Insights Informed Generative AI for Design' proposes a novel pipeline that integrates generative AI with real-world data to enrich AI-generated designs with sustainability metrics and material usage insights. The paper 'The Ethics of Generative AI in Anonymous Spaces' presents a characterization of AI-generated images shared on 4chan, highlighting concerning patterns in the use of this technology to create extremist content and bypass conventional content moderation systems.

Sources

"If we misunderstand the client, we misspend 100 hours": Exploring conversational AI and response types for information elicitation

Perspectives on Explanation Formats From Two Stakeholder Groups in Germany: Software Providers and Dairy Farmers

"I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products

The Ethics of Generative AI in Anonymous Spaces: A Case Study of 4chan's /pol/ Board

The Impact of Generative AI on Social Media: An Experimental Study

Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains

ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output

NTIRE 2025 Image Shadow Removal Challenge Report

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