Extracting Research Instruments from Educational Literature Using LLMs

TL;DR

This paper introduces an LLM-based system for extracting detailed research instrument information from educational literature, significantly improving accuracy and organization, thereby aiding researchers and policymakers in educational research and decision-making.

Contribution

The study develops a novel LLM-driven approach with multi-step prompting and a domain-specific schema for structured extraction of research instruments in education.

Findings

Outperforms existing methods in instrument name identification

Provides highly detailed and structured instrument data

Enhances accessibility and organization of research instruments

Abstract

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.