Multi-Tool Analysis of User Interface & Accessibility in Deployed Web-Based Chatbots
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| Format: | Preprint |
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2025
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| _version_ | 1866913877506129920 |
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| author | Rajmohan, Mukesh Desai, Smit Das, Sanchari |
| author_facet | Rajmohan, Mukesh Desai, Smit Das, Sanchari |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Tool Analysis of User Interface & Accessibility in Deployed Web-Based Chatbots Rajmohan, Mukesh Desai, Smit Das, Sanchari Human-Computer Interaction H.5.2; K.4.2 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. |
| title | Multi-Tool Analysis of User Interface & Accessibility in Deployed Web-Based Chatbots |
| topic | Human-Computer Interaction H.5.2; K.4.2 |
| url | https://arxiv.org/abs/2506.04659 |