From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews

TL;DR

This paper presents ARC, a tool designed to reduce cognitive load and enhance strategic exploration in systematic literature reviews by integrating multi-database search, transparent AI assistance, and external representations.

Contribution

The paper introduces ARC, a novel system that addresses key challenges in SLRs by enabling multi-database integration, transparent AI-assisted screening, and supporting strategic scholarly exploration.

Findings

ARC reduces administrative overhead for researchers.

Integrated environment promotes strategic exploration.

Supports verifiable AI reasoning in literature reviews.

Abstract

Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work. To investigate these challenges, we conducted an exploratory design study with 20 experienced researchers. This study identified key friction points: 1) the high cognitive load of managing iterative query refinement across multiple databases, 2) the overwhelming scale and pace of publication of modern literature, and 3) the tension between automation and scholarly agency. Informed by these findings, we developed ARC, a design probe that operationalizes solutions for multi-database integration, transparent iterative search, and verifiable AI-assisted screening. A comparative user study with 8 researchers suggests that an integrated environment facilitates a transition in scholarly work, moving researchers from managing administrative overhead to engaging in strategic exploration. By utilizing external representations to scaffold strategic exploration and transparent AI reasoning, our system supports verifiable judgment, aiming to augment expert contributions from initial creation through long-term maintenance of knowledge synthesis.