Task Mode: Dynamic Filtering for Task-Specific Web Navigation using LLMs

TL;DR

Task Mode leverages large language models to dynamically filter web content based on user goals, significantly improving navigation efficiency for screen reader users while maintaining performance for visual users.

Contribution

This paper introduces Task Mode, a novel system that uses LLMs to tailor web content filtering for different access needs, reducing accessibility disparities.

Findings

Reduced task completion time for screen reader users.

Maintained performance levels for visual users.

Decreased the time gap between user groups from 2x to 1.2x.

Abstract

Modern web interfaces are unnecessarily complex to use as they overwhelm users with excessive text and visuals unrelated to their current goals. This problem particularly impacts screen reader users (SRUs), who navigate content sequentially and may spend minutes traversing irrelevant elements before reaching desired information compared to vision users (VUs) who visually skim in seconds. We present Task Mode, a system that dynamically filters web content based on user-specified goals using large language models to identify and prioritize relevant elements while minimizing distractions. Our approach preserves page structure while offering multiple viewing modes tailored to different access needs. Our user study with 12 participants (6 VUs, 6 SRUs) demonstrates that our approach reduced task completion time for SRUs while maintaining performance for VUs, decreasing the completion time gap between groups from 2x to 1.2x. 11 of 12 participants wanted to use Task Mode in the future, reporting that Task Mode supported completing tasks with less effort and fewer distractions. This work demonstrates how designing new interactions simultaneously for visual and non-visual access can reduce rather than reinforce accessibility disparities in future technology created by human-computer interaction researchers and practitioners.