The Use of Generative Artificial Intelligence in Identifying and Extracting Single Case Participant Demographics Data

Abstract

Performing research syntheses can be challenging due to time, labor, and monetary costs. This manuscript presents the findings of two studies that investigate the usability and feasibility of artificial intelligence (AI) systems to assist with performing research syntheses. In Study 1, participant demographics data coded by humans were compared against data extracted using ChatGPT-4 and Google Bard from 67 single case research design (SCRD) studies in which infants, toddlers, and young children participated. Both AIs achieved interobserver agreement values above 96% for data related to participants’ names, ages, genders, race/ethnicities, and disabilities/conditions. Study 2 compared the time it took two human coders to extract participant demographics data from an additional 40 SCRD studies against ChatGPT-4. ChatGPT-4 extracted data from the studies faster than the human coders. Alhough both AIs may not be ready to autonomously perform research syntheses, findings suggest that both can assist researchers with the coding process.

Keywords

artificial intelligence AI ChatGPT Bard Gemini machine learning systematic review meta-analysis research syntheses

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