HiPaper: A Human-AI Collaborative System for Academic Literature Triage and Scholarly Sensemaking

Abstract

Scholarly sensemaking requires researchers to iteratively retrieve, collect, and synthesize literature as their understanding evolves. However, existing scholarly tools provide only partial support for this process. Recent artificial intelligence (AI)-based systems generally rely on opaque submit-and-wait pipelines that it users' ability to inspect, revise, and validate intermediate outputs. In this study, we present HiPaper, a human-AI collaborative literature review system informed by a formative study with academic experts. HiPaper organizes scholarly sensemaking as a three-stage interactive workflow spanning Retrieval, Collection, andSynthesis, centralized on the human-in-the-loop paradigm that exposes intermediate AI-generated content for user inspection and intervention through a persistent workspace called Knowledge Pocket. Through this workflow, users can iteratively refine search intent, curate and organize papers with AI-enriched metadata and clustering, and conduct evidence-traceable synthesis over selected literature. Our user study with 14 Ph.D. students showed that HiPaper efficiently supports scholarly sensemaking by enabling human-AI collaboration in both familiar and unfamiliar domains. Furthermore, the findings highlight how staged interactions with intermediate AI-generated content successfully scaffold user interactions throughout the sensemaking process.