Systematic reviews are among the core methods of empirical educational research, but they are time-consuming and often involve several thousand titles that need to be screened. For several years now, ASReview has been available as an open-source tool based on active learning methods that makes decision recommendations to make the screening process more efficient. Despite growing awareness, many researchers find it difficult to get started, partly due to unclear workflows, unresolved methodological questions or uncertainties regarding the use of AI-supported tools.

A tutorial paper, to which researchers from the Hector Institute for Empirical Educational Research and the LEAD Graduate School & Research Network have also contributed, addresses this issue and offers a clear yet scientifically precise introduction to ASReview.

The paper demonstrates how to install the tool and guides users through the entire screening process, from data preparation and model selection to the documentation of results.

The clear structure of the workflow is particularly helpful: the tutorial describes the necessary preparatory steps, how to create training data, and the available options – such as for algorithms, feature extraction or prioritisation. Guidance is also provided on transparent documentation, such as for pre-registration or methods sections in publications.

The researchers also emphasise that ASReview should not be understood as ‘black-box’ automation. Despite algorithmic support, key decisions – such as the final relevance assessment, quality assurance or determining an appropriate stopping point – remain with the researchers. The tutorial thus emphasises key principles of good scientific practice when using AI-supported tools.

For educational research and all disciplines in which reviews play a central role, the tutorial is more than just a technical user guide. It demonstrates how the efficiency of AI-supported screening procedures can be combined with the principles of good scientific practice. Those who already use ASReview or are looking for a well-founded introduction will find in this paper a structured guide that goes beyond mere operating instructions and makes the methodological core of active learning understandable.

Publication:
https://doi.org/10.1177/25152459261442150

See also:
Fütterer, T., Campos, D. G., Gfrörer, T., Lavelle-Hill, R., Murayama, K. & Scherer, R. (2025). AI tools for systematic literature reviews and meta-analyses in educational psychology: An overview and a practical guide. Learning And Individual Differences, 126, 102849. https://doi.org/10.1016/j.lindif.2025.102849 

Press contact:
presse@lead.uni-tuebingen.de