Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages
This addresses the challenge of cognitive and manual effort in web browsing for users handling distributed information, though it appears incremental as it builds on existing AI and interaction concepts.
The authors tackled the problem of managing and synthesizing large volumes of information across webpages by developing Orca, a prototype web browser that uses AI to augment user-driven interactions, resulting in increased information foraging, enhanced user control, and more flexible sensemaking.
Web-based activities are fundamentally distributed across webpages. However, conventional browsers with stacks of tabs fail to support operating and synthesizing large volumes of information across pages. While recent AI systems enable fully automated web browsing and information synthesis, they often diminish user agency and hinder contextual understanding. Therefore, we explore how AI could instead augment users' interactions with content across webpages and mitigate cognitive and manual efforts. Through literature on information tasks and web browsing challenges, and an iterative design process, we present a rich set of novel interactions with our prototype web browser, Orca. Leveraging AI, Orca supports user-driven exploration, operation, organization, and synthesis of web content at scale. To enable browsing at scale, webpages are treated as malleable materials that humans and AI can collaboratively manipulate and compose into a malleable, dynamic, and browser-level workspace. Our evaluation revealed an increased "appetite" for information foraging, enhanced user control, and more flexibility in sensemaking across a broader information landscape on the web.