SWARM-SLR AIssistant: A Unified Framework for Scalable Systematic Literature Review Automation

TL;DR

The paper introduces SWARM-SLR AIssistant, a modular, AI-guided framework that enhances systematic literature review automation by integrating tools, supporting conversational workflows, and proposing a centralized tool registry for improved usability and scalability.

Contribution

It presents a novel AI-assisted framework combining structured methodology with agent-based support and a shared tool registry for scalable SLR automation.

Findings

Improved usability demonstrated in preliminary evaluation.

Integration of LLM-guided support enhances workflow.

Centralized tool registry facilitates tool assessment and registration.

Abstract

Despite a growing ecosystem of tools supporting Systematic Literature Reviews (SLRs), integrating them into user-friendly workflows remains challenging. The Streamlined Workflow for Automating Machine-Actionable Systematic Literature Reviews (SWARM-SLR) unified the tool annotation and provided a cohesive yet modular workflow, but faced scalability and usability issues. We introduce the SWARM-SLR AIssistant, a unified framework that combines the SWARM-SLR's structured methodology with an agent-based assistant that integrates research tools in a modular interface. The first SWARM-SLR stage is integrated, enabling conversational, LLM-guided support and persistent data storage. To address the tool assessment bottleneck, we propose a centralized tool registry that allows developers to annotate and register tools autonomously using a shared metadata schema. Preliminary evaluation shows improved usability, but challenges remain in balancing efficiency, accessibility, and transparency. Further development is needed to realize scalable SLR automation.