交互式证据地图用于可视化和理解系统综述
Interactive Evidence Maps for Visualizing and Understanding Systematic Reviews
AI总结：
本文提出交互式证据地图，通过大语言模型提取主题模型，帮助研究人员动态探索和分析综述数据，提升透明度和发现文献中的模式与空白。
摘要：
系统综述提供研究领域的全面综合。因此，系统综述往往强调跨大量文献的综合，而非仅描述得出结论的研究。这可能导致对样本的不完整描述，鼓励结论的过度泛化，掩盖现有工作的联系，或掩盖文献中的空白。为解决这一挑战，我们引入交互式证据地图；一种可访问的可视化工具，使研究人员能够动态探索、过滤和分析综述数据。我们的方法利用大语言模型提取主题模型，将异质综述数据结构化为一个交互式、可探索的知识图谱，支持超越静态表格和图表的深入检查。我们通过一篇已发表的关于K-12教育中教学代理的范围综述的数据，展示了交互式证据地图的实用性，并将其结果与范围综述报告的结果进行比较。结果表明，交互式证据地图通过增强透明度、支持探索性分析和揭示可能难以通过叙述摘要单独发现的模式和空白，补充了传统综合方法。
英文摘要：
Systematic reviews provide comprehensive syntheses of research fields. As a result, systematic reviews often emphasize synthesizing across the large bodies of literature rather than just describing the studies from which the conclusions were drawn. This risks an incomplete description of the sample - encouraging overgeneralization of the findings, obscuring connections between existing work, or overshadowing gaps in the literature. To address this challenge, we introduce interactive evidence maps; an accessible visualization tool that enables researchers to explore, filter, and analyze review data dynamically. Our approach leverages large language models to extract topic models that structure heterogeneous review data into an interactive, explorable knowledge map that supports deeper inspection beyond static tables and figures. We demonstrate the usefulness of interactive evidence maps using data from a published scoping review of pedagogical agents in K-12 education, and compare the results of the evidence map to those reported in the scoping review. Results show that interactive evidence maps complement traditional syntheses by enhancing transparency, supporting exploratory analysis, and revealing patterns and gaps that may not be easy to detect through narrative summaries alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
