Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912838227853312 |
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| author | Huang, Yanwei Narechania, Arpit |
| author_facet | Huang, Yanwei Narechania, Arpit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15100 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek Huang, Yanwei Narechania, Arpit Human-Computer Interaction 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. |
| title | Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.15100 |