5 papers
Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems
Mengli, Duan, Yuhe +3
Multimodal Large Language Models (MLLMs) are increasingly used to interpret visualizations, yet little is known about why they fail. We present the first systematic analysis of bar…
Theory is Shapes
Matthew Varona, Maryam Hedayati, Matthew Kay +1
"Theory figures" are a staple of theoretical visualization research. Common shapes such as Cartesian planes and flowcharts can be used not only to explain conceptual contributions,…
The State of the Art in Visualization Literacy
Matthew Varona, Karen Bonilla, Maryam Hedayati +4
Research in visualization literacy explores the skills required to engage with visualizations. This state-of-the-art report surveys the current literature in visualization literacy…
ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking
Runlong Ye, Patrick Yung Kang Lee, Matthew Varona +2
Synthesizing knowledge from large document collections is a critical yet increasingly complex aspect of qualitative research and knowledge work. While AI offers automation potentia…
The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity
Runlong Ye, Matthew Varona, Oliver Huang +3
Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. While promising for augmenting cognition and…