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.

翻译：系统性综述提供研究领域的综合合成。因此，系统性综述通常强调跨大量文献的综合，而非仅描述得出研究结论的具体研究。这可能导致对样本的不完整描述——助长研究发现的过度泛化、模糊现有工作间的联系，或掩盖文献中的空白。针对这一挑战，我们引入交互式证据图谱：一种可访问的可视化工具，使研究者能够动态地探索、筛选和分析综述数据。我们的方法利用大型语言模型提取主题模型，将异构综述数据结构化为交互式可探索的知识图谱，支持超越静态表格与图表的深度审视。我们利用一项关于K-12教育中教学代理的已发表范围综述数据，展示了交互式证据图谱的实用性，并将其结果与原始范围综述进行了比较。结果表明，交互式证据图谱通过增强透明性、支持探索性分析，并揭示通过叙事总结难以发现的模式与空白，对传统综合方法形成了补充。