Visualization researchers utilize workshops both for applied research and to engage different populations with visualization‐based activities. While there are many benefits to running visualization workshops, their utility and impact rely on the presence of a researcher who has deep knowledge about visualization theory and practice. In this work, we introduce workshop‐in‐a‐box as a design concept intended to challenge the researcher‐centric approach to data physicalization workshops. Through a design study with a socially innovative organization, we deployed several data physicalization workshops that our collaborator ran instead of us. Based on this experience, along with two accompanying case studies that validate the concept, we present material and procedural considerations for how to put data physicalization workshops into a box and the implications it has for extending visualization research outside the bounds of academia.
These findings show that Vis designers actively use LLMs for both creative and technical aspects of the visualization process, and opens up opportunities for research combining LLM-mediated work with Vis tools that incorporate data visualization guidance, constraints, and best practices.
S. C. Spivak, Aditi Krishna, Mahsan Nourani et al.· IEEE Computer Graphics and A...· 0 citations
Traditional visualization approaches often fail because data scientists lack understanding of practitioner tasks, and educators lack data expertise, leading to poor tool adoption. This paper aims to offer insights from a mid-sized urban school district in the Northeastern USA, where a data dashboard designer collab...
Alden P. Burnham, Alex J. Bowers· Information and Learning Sci...· 0 citations
We report on a case study for designing authoring environments for interactive visualizations to enhance scientific work. We conducted a workshop and prototype review with a group of marine biologists. When it came to visualizing their data, participants identified challenges in conveying their research accurately and...
Caroline Berger, Josh Pollock, Dylan Wooton et al.· 0 citations
A closer look at a broader set of terms that can express desirable results around visualization research, and untangle how these words are understood in the community in relation to other similar terms.
A. Chinni, Md Dilshadur Rahman, Bon Adriel Aseniero et al.· 0 citations
We present CARE-HCI, an interactive visualisation tool built to support literature exploration, stemming from the needs during our own review process. By integrating paper data and citation relationships with timeline and graph-based overviews, it offers researchers an intuitive way to visually navigate and make sense...
Zi-Xuan Wang, Sheng-Chen Zhang, Xin-Huan Shu· Adjunct Proceedings of the 1...· 0 citations
The findings provide the first systematic assessment of vibe coding for interactive visualization construction, revealing both its strengths and limitations, explaining the shift in developer labor and identifying the hybrid collaboration strategies participants adopted.
Yan-Shan Zeng, Ruixuan Tu, Zuo-Dong Xiang et al.· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
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