触摸还是聊天：大语言模型和触觉图表对视力障碍者学习复杂图表类型的效用
Touching or Chatting: The Utility of LLMs and Tactile Charts for Learning about Complex Chart Types by BLV Individuals
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中文总结 AI 辅助
研究探讨大语言模型对视力障碍者图表类型学习的影响及触觉学习的作用，通过扩展触觉图表学习工具并比较两种学习形式，发现触觉模板有助于形成心理模型，利于后续数据探索，无触觉支持的文本加LLM解释在空间推理任务中有弱点。
中文摘要 AI 辅助
可视化对于数据交流至关重要，但盲人和低视力（BLV）人群在理解图表类型方面常缺乏支持。先前研究发现BLV个体认为示例触觉图表比纯文本方法更有用。大语言模型（LLMs）越来越多地被BLV个体用于图表解释和问答，但主要用于数据集探索而非图表类型学习。我们研究LLMs如何影响图表类型学习以及触觉学习是否能改善后续LLM支持的探索。我们扩展了触觉图表学习工具，加入LLM聊天机器人。在对12名BLV参与者的访谈研究中，比较了两种学习形式。主题分析表明，触觉模板支持BLV参与者形成图表类型心理模型，为后续LLM介导的数据探索搭建了框架。没有触觉支持的文本+LLM解释在空间推理任务中显示出弱点。
英文摘要
Visualizations are central to communicating data, yet blind and low-vision (BLV) people often lack support for understanding chart types---knowledge that is essential for interpreting new visualizations and collaborating with sighted peers. Prior work found that BLV individuals viewed example tactile charts as more helpful than text-only approaches and preferred them for learning advanced chart types, particularly for understanding spatial layouts and shapes. Meanwhile, large language models (LLMs) are increasingly used by BLV individuals for chart explanation and question answering (QA), but have been studied primarily for dataset exploration rather than chart-type learning. Existing LLM-based chart QA also shows that users frequently ask about layout and structure, yet struggle with spatial concepts and misdirect questions when mental models are weak. We investigate how LLMs influence chart-type learning and whether tactile learning improves subsequent LLM-supported exploration. We extend our tactile chart learning tools with an LLM chatbot that provides interactive explanations and supports follow-up questions. In an interview study with 12 BLV participants, we compare two learning formats: (1) a tactile chart, a textual explanation, and an LLM chatbot; and (2) a textual explanation and an LLM chatbot. The learning phase was followed by exploration of an unfamiliar dataset using alt text and an LLM. Thematic analysis shows that tactile templates support BLV participants' formation of chart-type mental models, which scaffolds subsequent LLM-mediated data exploration. Text+LLM explanations without tactile support show weaknesses for spatial-reasoning tasks.
