用大模型自动提取教育研究工具信息，提升文献管理效率
Extracting Research Instruments from Educational Literature Using LLMs
- 采用多步提示与领域专用数据格式，结构化提取研究工具信息
- 在识别工具名称和详细信息上显著优于传统方法
- 适合教育研究者、政策制定者快速获取工具数据
大型语言模型正在改变学术文献的信息提取方式，为知识管理带来新可能。本研究提出一个基于LLM的系统，用于从教育领域文献中提取研究工具的详细信息，包括名称、类型、目标被试、测量构念及结果。通过多步提示与领域特定数据模式，生成优化后的结构化输出，适用于教育研究。评估表明，该系统在识别工具名称及详细信息方面显著优于其他方法，展示了大模型在教育语境下信息提取的潜力。大规模聚合此类信息可提升研究人员与教育决策者的获取便利性，支持更科学的研究与政策制定。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are transforming information extraction from academic literature, offering new possibilities for knowledge management. This study presents an LLM-based system designed to extract detailed information about research instruments used in the education field, including their names, types, target respondents, measured constructs, and outcomes. Using multi-step prompting and a domain-specific data schema, it generates structured outputs optimized for educational research. Our evaluation shows that this system significantly outperforms other approaches, particularly in identifying instrument names and detailed information. This demonstrates the potential of LLM-powered information extraction in educational contexts, offering a systematic way to organize research instrument information. The ability to aggregate such information at scale enhances accessibility for researchers and education leaders, facilitating informed decision-making in educational research and policy.
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