Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek

TL;DR

WebSeek is a browser extension that combines user-driven data extraction and analysis with AI-provided proactive and reactive guidance, enhancing web-based decision-making and data exploration.

Contribution

It introduces a mixed-initiative environment for interactive data analysis on the web, integrating AI guidance with user control and data manipulation capabilities.

Findings

Participants showed diverse analysis strategies.

Users value transparency and control in AI collaboration.

WebSeek facilitates flexible data transformations and visualizations.

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.