LitFlow: An integrated, AI-augmented systematic literature review platform

Näscher, Hans-Henning; Strohmann, Timo; vom Brocke, Jan

Abstract

Systematic literature reviews (SLRs) are central to rigorous research but remain resource-intensive and dependent on fragmented toolchains. At the same time, artificial intelligence (AI)-based support for review tasks often lacks transparency and limits researcher control. This paper presents LitFlow, a web- based platform for AI-augmented SLRs developed following the echeloned de- sign science research (eDSR) methodology. LitFlow integrates multi-database search, criteria-based screening, structured data extraction, and audit-trail gener- ation within a single workspace. Its augmentation approach provides AI recom- mendations with confidence scores, justifications, and source references, while final decisions remain with the researcher. The platform is built on a community- extensible architecture. A formative evaluation with five researchers confirmed the perceived value of the integrated workflow and the augmentation-oriented design. Participants also raised socio-technical concerns, including potential an- choring effects from AI recommendations, which inform directions for future it- erations. LitFlow contributes a working demonstration of transparent, researcher- controlled AI support across the full SLR workflow.