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.

Keywords

Systematic Literature Review, AI Augmentation, Design Science Research

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