[论文解读] VizQStudio: Iterative Visualization Literacy MCQs Design with Simulated Students

Zixin Chen, Yuhang Zeng+5 more|arXiv (Cornell University)|Mar 1, 2026

Intelligent Tutoring Systems and Adaptive Learning被引用 0

一句话总结

VizQStudio 是一个可视化分析系统，利用 MLLM 驱动的仿真学生使讲师能够在设计阶段迭代地设计多模态可视化素养 MCQ，提供设计时反馈、误解检测和改进的问题生成。

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 MLLM-powered simulated students. Instructors can specify diverse student profiles spanning demographics, knowledge levels, and learning-related traits. The system then visualizes how simulated students reason about and respond to different question components, helping instructors explore potential misconceptions, difficulty calibration, and design trade-offs prior to classroom deployment. We investigate VizQStudio through a mixed-method evaluation, including expert interviews, case studies, a classroom deployment, and a large-scale online study. Overall, this work reframes MLLM-based student simulation in assessment authoring as a design-time, exploratory aid. By examining both its value and limitations in realistic instructional settings, we surface design insights that inform how future systems can support instructor-centered, iterative, and responsible uses of AI for multimodal assessment design in visualization literacy and related domains.

研究动机与目标

• 识别教师在设计可视化素养 MCQ 和仿真反馈方面的需求。
• 提供支持 AI 基于学生仿真的迭代 MCQ 设计的可视化分析工作流。
• Enable exploration of misconceptions, difficulty calibration, and design trade-offs before classroom deployment.
• 支持在题目设计中考虑多样化学习者档案与认知因素。
• 通过专家访谈、案例研究、课堂部署与在线研究来评估系统。

提出的方法

• 使用形成性研究来提取 MCQ 设计和学生档案的设计需求与特征。
• 开发包含五个面板（Sample、Feature、Simulation、Agent Profile、Agent Setting）和一个 Simulation View 的 VizQStudio。
• 实现一个基于 MLLM 的学生仿真模块，生成档案、聚类和响应。
• 采用基于模板、检索增强生成的流水线，使用 D3.js 模板生成图表。
• 提供实时、可视化的对仿真推理路径和群体级分析的反馈，以指导迭代改进。

实验结果

研究问题

• RQ1 可视化教育工作者在 MCQ 设计和仿真反馈方面有哪些设计需求？
• RQ2 如何让 AI 驱动的仿真学生模型在可视化素养中实现迭代、多模态 MCQ 设计？
• RQ3 VizQStudio 是否能够在多样化学习者档案下实现对 MCQ 的有效、可扩展的设计时评估？
• RQ4 在将 MLLMs 集成到可视化素养评估撰写中时，存在的局限性与设计考量是什么？

主要发现

• 使用 VizQStudio 设计的 MCQ 在学习成果方面与既定基准问题相当。
• 该系统使 MCQ 设计过程具有更大的灵活性和可扩展性。
• 仿真学生反馈有助于识别误解并为迭代改进提供信息。
• 基于 D3.js 模板的生成配合类似检索增强（RAG）的方式提升了稳定性与图表质量。
• 学生档案与聚类为针对性的问题调整提供了群体级洞见。
• 设计时仿真澄清了难度、情境和视觉编码之间的权衡。