SearcharxivSearch

arXiv subjects

Kathrin Schnizer

Publications and source records attributed to Kathrin Schnizer.

2 recordsLinked to original sources

From Scores to Strategies: Towards Gaze-Informed Diagnostic Assessment for Visualization Literacy

Visualization literacy assessments typically rely on correctness to classify performance, providing little evidence about how readers arrive at their answers. We argue that gaze can address this gap as an implicit process signal that complements standardized tests without sacrificing their scalability. Synthesizing findings from visualization and related research, we show that gaze metrics capture cognitive load invisible to accuracy and response time, and reflect strategy differences in attention allocation that track proficiency. We propose assessments that integrate literacy scores with gaze-derived process indicators - component-level attention profiles, integration frequency, and viewing path dispersion - to distinguish fluent comprehension from labored success. This would shift literacy assessment from binary classification toward nuanced characterization of how readers navigate, integrate, and coordinate information across chart components. A roadmap identifies open challenges in empirical grounding, generalizability, assessment design, and practical feasibility.

cs.HC

User-Centered AI for Data Exploration: Rethinking GenAI's Role in Visualization

Recent advances in GenAI have enabled automation in data visualization, allowing users to generate visual representations using natural language. However, existing systems primarily focus on automation, overlooking users' varying expertise levels and analytical needs. In this position paper, we advocate for a shift toward adaptive GenAI-driven visualization tools that tailor interactions, reasoning, and visualizations to individual users. We first review existing automation-focused approaches and highlight their limitations. We then introduce methods for assessing user expertise, as well as key open challenges and research questions that must be addressed to allow for an adaptive approach. Finally, we present our vision for a user-centered system that leverages GenAI not only for automation but as an intelligent collaborator in visual data exploration. Our perspective contributes to the broader discussion on designing GenAI-based systems that enhance human cognition by dynamically adapting to the user, ultimately advancing toward systems that promote augmented cognition.

cs.HC