WebSeek: Facilitating Proactive and Reactive Guidance for Decision Making on the Web | ACM CHI 2026 скачать в хорошем качестве

WebSeek: Facilitating Proactive and Reactive Guidance for Decision Making on the Web | ACM CHI 2026

Authors: Yanwei Huang and Arpit Narechania Affiliations: The Hong Kong University of Science and Technology Abstract: Web AI agents such as ChatGPT Agent and GenSpark are increasingly used for routine web-based tasks, yet they still rely on text-based input prompts, lack proactive detection of user intent, and offer no support for interactive data analysis and decision making. We present WebSeek, a mixed-initiative browser extension that enables users to discover and extract information from webpages to then flexibly build, transform, and refine tangible data artifacts–such as tables, lists, and visualizations–all within an interactive canvas. Within this environment, users can perform analysis–including data transformations such as joining tables or creating visualizations–while an in-built AI both proactively offers context-aware guidance and automation, and reactively responds to explicit user requests. An exploratory user study (N=15) with WebSeek as a probe reveals participants' diverse analysis strategies, underscoring their desire for transparency and control during human-AI collaboration.