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

2026-01-21

AI Summary

Current Web-based AI agents predominantly rely on passive textual prompts, limiting their ability to proactively infer user intent and support interactive data analysis and decision-making. To address this gap, this work proposes WebSeek—a hybrid proactive browser extension that enables users to extract data from web pages and construct, transform, and refine tabular, list-based, and visual artifacts on an interactive canvas. WebSeek integrates context-aware AI to provide both proactive guidance and responsive assistance, while prioritizing transparency and user control. This study presents the first integration of proactive and reactive AI guidance within a web browsing environment. An exploratory user study with 15 participants demonstrates the system’s capacity to support diverse analytical strategies and underscores the critical need for controllability and explainability in human-AI collaboration.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Web AI agents
• proactive intent detection
• interactive data analysis
• decision making
• human-AI collaboration

Innovation

• mixed-initiative interaction
• proactive AI guidance
• interactive data artifacts
• web-based decision making
• human-AI collaboration