Multi-Tool Analysis of User Interface & Accessibility in Deployed Web-Based Chatbots

TL;DR

This study conducts a comprehensive multi-tool evaluation of 106 deployed web-based chatbots across various domains, revealing prevalent accessibility issues and emphasizing the importance of multi-tool assessments for improving user-friendly interfaces.

Contribution

It introduces a systematic multi-tool evaluation methodology for chatbot accessibility and performance, providing new insights and actionable recommendations for developers.

Findings

Over 80% of chatbots have at least one critical accessibility issue.

Accessibility scores are highly correlated across tools, but performance scores are not.

The study offers a replicable evaluation framework and practical recommendations.

Abstract

In this work, we present a multi-tool evaluation of 106 deployed web-based chatbots, across domains like healthcare, education and customer service, comprising both standalone applications and embedded widgets using automated tools (Google Lighthouse, PageSpeed Insights, SiteImprove Accessibility Checker) and manual audits (Microsoft Accessibility Insights). Our analysis reveals that over 80% of chatbots exhibit at least one critical accessibility issue, and 45% suffer from missing semantic structures or ARIA role misuse. Furthermore, we found that accessibility scores correlate strongly across tools (e.g., Lighthouse vs PageSpeed Insights, r = 0.861), but performance scores do not (r = 0.436), underscoring the value of a multi-tool approach. We offer a replicable evaluation insights and actionable recommendations to support the development of user-friendly conversational interfaces.