{"title":"从数据故事到对话：生成式人工智能代理和数据故事在增强数据可视化理解方面的随机对照试验","authors":"Lixiang Yan, Roberto Martinez-Maldonado, Yueqiao Jin, Vanessa Echeverria, Mikaela Milesi, Jie Fan, Linxuan Zhao, Riordan Alfredo, Xinyu Li, Dragan Gašević","doi":"arxiv-2409.11645","DOIUrl":null,"url":null,"abstract":"Generative AI (GenAI) agents offer a potentially scalable approach to support\ncomprehending complex data visualisations, a skill many individuals struggle\nwith. While data storytelling has proven effective, there is little evidence\nregarding the comparative effectiveness of GenAI agents. To address this gap,\nwe conducted a randomised controlled study with 141 participants to compare the\neffectiveness and efficiency of data dialogues facilitated by both passive\n(which simply answer participants' questions about visualisations) and\nproactive (infused with scaffolding questions to guide participants through\nvisualisations) GenAI agents against data storytelling in enhancing their\ncomprehension of data visualisations. Comprehension was measured before,\nduring, and after the intervention. Results suggest that passive GenAI agents\nimprove comprehension similarly to data storytelling both during and after\nintervention. Notably, proactive GenAI agents significantly enhance\ncomprehension after intervention compared to both passive GenAI agents and\nstandalone data storytelling, regardless of participants' visualisation\nliteracy, indicating sustained improvements and learning.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From Data Stories to Dialogues: A Randomised Controlled Trial of Generative AI Agents and Data Storytelling in Enhancing Data Visualisation Comprehension\",\"authors\":\"Lixiang Yan, Roberto Martinez-Maldonado, Yueqiao Jin, Vanessa Echeverria, Mikaela Milesi, Jie Fan, Linxuan Zhao, Riordan Alfredo, Xinyu Li, Dragan Gašević\",\"doi\":\"arxiv-2409.11645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generative AI (GenAI) agents offer a potentially scalable approach to support\\ncomprehending complex data visualisations, a skill many individuals struggle\\nwith. While data storytelling has proven effective, there is little evidence\\nregarding the comparative effectiveness of GenAI agents. To address this gap,\\nwe conducted a randomised controlled study with 141 participants to compare the\\neffectiveness and efficiency of data dialogues facilitated by both passive\\n(which simply answer participants' questions about visualisations) and\\nproactive (infused with scaffolding questions to guide participants through\\nvisualisations) GenAI agents against data storytelling in enhancing their\\ncomprehension of data visualisations. Comprehension was measured before,\\nduring, and after the intervention. Results suggest that passive GenAI agents\\nimprove comprehension similarly to data storytelling both during and after\\nintervention. Notably, proactive GenAI agents significantly enhance\\ncomprehension after intervention compared to both passive GenAI agents and\\nstandalone data storytelling, regardless of participants' visualisation\\nliteracy, indicating sustained improvements and learning.\",\"PeriodicalId\":501541,\"journal\":{\"name\":\"arXiv - CS - Human-Computer Interaction\",\"volume\":\"43 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Human-Computer Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11645\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Human-Computer Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11645","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
From Data Stories to Dialogues: A Randomised Controlled Trial of Generative AI Agents and Data Storytelling in Enhancing Data Visualisation Comprehension
Generative AI (GenAI) agents offer a potentially scalable approach to support
comprehending complex data visualisations, a skill many individuals struggle
with. While data storytelling has proven effective, there is little evidence
regarding the comparative effectiveness of GenAI agents. To address this gap,
we conducted a randomised controlled study with 141 participants to compare the
effectiveness and efficiency of data dialogues facilitated by both passive
(which simply answer participants' questions about visualisations) and
proactive (infused with scaffolding questions to guide participants through
visualisations) GenAI agents against data storytelling in enhancing their
comprehension of data visualisations. Comprehension was measured before,
during, and after the intervention. Results suggest that passive GenAI agents
improve comprehension similarly to data storytelling both during and after
intervention. Notably, proactive GenAI agents significantly enhance
comprehension after intervention compared to both passive GenAI agents and
standalone data storytelling, regardless of participants' visualisation
literacy, indicating sustained improvements and learning.
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