Alice Ferng | Search arXiv

arXiv2026

What Makes a Great Co-Worker in an AI-Native Workplace?

As knowledge work grows interdependent between humans and AI, we ask what makes a great co-worker in an AI-native workplace. To answer this, we conducted 22 interviews and a large-scale mixed-methods survey of 1,534 knowledge workers at a multinational technology company. We contribute BACI, a framework of 75 co-worker qualities that apply to humans and AI, spanning Benevolence, Ability, Cooperativeness, and Integrity. Comparing priorities for humans and AI identified 11 co-worker archetypes and revealed disagreement over whether AI should have warmth, take initiative, or own outcomes. We also show how priorities for these archetypes varied with workers' individual characteristics. Lastly, we contribute a taxonomy of AI work etiquette capturing the obligations co-workers expect of one another when preparing, sharing, and taking responsibility for AI-supported work. Based on these findings, we derive implications to inform worker-centric AI and workplace design.

cs.HC↗

arXiv2026

VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization

Enterprise chatbots show promise in supporting knowledge workers in information synthesis tasks by retrieving context from large, heterogeneous databases before generating answers. However, when the retrieved context misaligns with user intentions, the chatbot often produces "irrelevantly right" responses that provide little value. In this work, we introduce VizCopilot, a prototype that incorporates visualization techniques to actively involve end-users in context alignment. By combining topic modeling with document visualization, VizCopilot enables human oversight and modification of retrieved context while keeping cognitive overhead manageable. We used VizCopilot as a design probe in a Research-through-Design study to evaluate the role of visualization in context alignment and to surface future design opportunities. Our findings show that visualization not only helps users detect and correct misaligned context but also encourages them to adapt their prompting strategies, enabling the system to retrieve more relevant context from the outset. At the same time, the study reveals limitations in verification support regarding close-reading and trust in AI summaries. We outline future directions for visualization-enhanced chatbots, focusing on personalization, proactivity, and sustainable human-AI collaboration.

cs.HC↗