LitFlow: An integrated, AI-augmented systematic literature review platform
Zusammenfassung
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.
Schlüsselwörter
Systematic Literature Review, AI Augmentation, Design Science Research