Literature review workspace enhances human AI collaboration awareness
Ream: Unfolding Mutual Awareness in Human-Agent Workspaces
Artificial IntelligenceHuman-Computer Interaction
Summary
When humans and AI agents work together on tasks like reviewing research papers, it can be hard for both sides to keep track of what the other is doing. This is especially true because AI can work very quickly, and people don’t always explain what they are focusing on. The authors created Ream, a tool that shows what both the human and AI have done on shared documents, helping them understand and coordinate with each other better. In tests with researchers, this made it easier to check evidence, guide the AI, and stay on the same page.
What this means in practice
- •For knowledge workers: Integrate detailed activity tracking in document-based workflows to improve coordination between human workers and AI assistants.
- •For technical writers: Use shared annotations and visual histories to enhance communication and maintain focus when collaborating with AI tools on large texts.
Authors
Peiling Jiang, Sangho Suh, Varsha Kishore, Jonathan Bragg, Haijun Xia, Pao Siangliulue, Daniel S. Weld, Amy X. Zhang, Joseph Chee Chang
Abstract
As AI agents work alongside humans in shared workspaces, a mutual awareness challenge arises: agents act at speeds that outpace human monitoring, and users' evolving interests are not always expressed in chat. This challenge is especially pressing in literature review, where both parties retrieve, read, and synthesize a growing body of papers. We present Ream, a literature review workspace that supports mutual awareness through structured artifacts, bidirectional engagement tracking, and localized visualizations. Users can see each party's activity within these documents, and agents can retrieve the same history to guide their work. In studies with eighteen researchers, participants used these traces to inspect evidence, steer agents, communicate through annotations, and reflect on their research focus. Shared histories also helped agents build on earlier work. These findings inform how engagement traces within shared documents can support transparency, personalized assistance, and coordination in human-agent knowledge work.