Simulation-based active learning for systematic reviews: A scoping review of literature
Year: 2025
Type: review
Abstract: Background: Active learning is a proposed method for accelerating the screening phase of systematic reviews. While extensively studied, evidence remains scattered across a fragmented body of literature. Objective: This scoping review investigates whether active learning is recommended for systematic review screening and identifies areas needing further research. Design: We screened 1887 records published since 2006 using ASReview, an active learning tool, and included 60 relevant studies. We also analysed 238 of 336 collected datasets for study design, dataset usage, and implementation. Results: All 60 studies recommended active learning as a means to improve screening efficiency. Despite some methodological heterogeneity, consistent endorsement was found across the literature. Conclusion: Active learning shows strong potential to support systematic review screening. Standardising evaluation metrics, encouraging open data practices, and diversifying model configurations are key priorities for advancing this field.
Source: Journal of Information Science
Authors: Jelle Jasper Teijema, Guilherme Ribeiro, Sofie Seuren, Daniel Anadria, Ayoub Bagheri, Rens van de Schoot
Institutions: Utrecht University, University of Lisbon
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W4417449083