VizQStudio: Iterative Visualization Literacy MCQs Design with Simulated Students
Zixin Chen, Yuhang Zeng, Sicheng Song, Yanna Lin, Xian Xu, Huamin Qu, Meng Xia

TL;DR
VizQStudio is a visual analytics tool that leverages MLLM-powered simulated students to assist instructors in designing, refining, and understanding visualization literacy MCQs through iterative, exploratory processes before classroom use.
Contribution
The paper introduces VizQStudio, a novel system that enables iterative visualization literacy MCQ design using simulated students, addressing limitations of fixed item banks and supporting instructor-centered assessment creation.
Findings
Effective exploration of question design trade-offs
Identification of misconceptions and difficulty calibration
Insights into AI-assisted assessment design limitations
Abstract
Multiple-choice questions (MCQs) are a widely used educational tool, particularly in domains such as visualization literacy that require broad conceptual coverage and support diverse real-world applications. However, designing high-quality visualization literacy MCQs remains challenging, as instructors must coordinate multimodal elements (e.g., charts, question stems, and distractors), address diverse visualization tasks, and accommodate learners with heterogeneous backgrounds. Existing visualization literacy assessments primarily rely on standardized, fixed item banks, offering limited support for iterative question design that adapts to differences in learners' abilities, backgrounds, and reasoning strategies. To address these challenges, we present VizQStudio, a visual analytics system that supports instructors in iteratively designing and refining visualization literacy MCQs using…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Data Visualization and Analytics · Innovative Teaching and Learning Methods
