Artificial intelligence as a partner in meta-analysis—Research agenda, user recommendations, and speed–accuracy tradeoffs: Commentary on Jansen et al. (2025).

Using a metascience framework for improving meta-analyses, Jansen et al. (2025) tested the accuracy and efficiency of data extraction from primary studies used in meta-analyses with a range of large language models. Efficiency was impressive: Across thousands of studies and hundreds of variables, eight large language models took less than an hour combined to extract hundreds of thousands of data points-work estimated to take a human coder >6,500 hr. Nevertheless, accuracy was inconsistent, ranging from high to low depending on the variable. From these results, Jansen et al. recommended (a) a research agenda for investigating the use of artificial intelligence (AI) for data extraction and (b) methods for using AI as a partner for data extraction when conducting systematic reviews. This commentary expands on recommendations for the research agenda, such as investigating AI-induced bias, the illusion of exploratory depth, and using AI to extract study quality data. This commentary also offers further considerations regarding using AI as a meta-analysis partner, such as how iterative prompts might reduce coding independence. Finally, the commentary discusses speed-accuracy tradeoffs in meta-analyses. (PsycInfo Database Record (c) 2026 APA, all rights reserved).