## 2 Background
### Related Work  
Auditors of website and web application quality have an increasing palette of AI tools at their disposal (Table 2). [PERSON] et al. [6] tested ChatGPT's ability to identify the level of compliance of two websites with the Web Content Accessibility Guidelines (WCAGs) and address any related issues. They employed the Web Accessibility Evaluation Tools (WAVE) in their comparisons. They demonstrated that ChatGPT can improve WCAG website optimisation. [PERSON] & [PERSON] [2] designed highly effective LLM scripts for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & \multicolumn{2}{c}{**ChatGPT AI Plugins**} & **Web Applications** & **Reference** \\ \hline User interaction & (a) & Audit is initiated with a & (a) & Audit is initiated with a CTA button. & [15; 30; 31] \\  & subjective prompt. & (b) & It is run as an independent program & \\  & (b) & It is run as a conversational interface & or web browser component. & \\  & for real-time interaction. ChatGPT & Delivers a standardised audit report. & \\  & can discuss measurement results & It cannot discuss the results in & \\  & according to user-submitted & real time. & \\  & questions (prompts). & & \\ \hline Time and functional details & It requires a context and user expectations of the audit to be provided. The analysis is performed in real time and controlled & The tests follow a standardised scenario. & [15; 29; 30; 32], \\  & with prompts. & & \\ \hline Primary scope of analysis & The plugin evaluates the content, structure, and web accessibility of the website in a more descriptive manner. It puts the user first and can handle & It focuses on technical aspects, such as performance, SEO, accessibility, and code syntax, according to W3C. & [32; 33] \\  & non-standard prompts. & & \\ \hline Evaluation methods & ChatGPT uses language models to analyse selected quality criteria. & It employs automatic tests with predefined criteria based on metrics, such as Core Web Vitals. & [12; 15; 34] \\ \hline Results and reporting & It composes detailed, narrated reports pertinent to user questions with instances of recommendations. & It generates an automated report with detailed measurement results (0–100) for each evaluated category. & [15; 34] \\ \hline Visualisation of results & Narrative, descriptive reports, tables, charts, and popular science infographics. & Waterfall and pie charts, bar graphs, and others. The results are presented using & [6; 15; 34; 35] \\  & & & \\  & & & \\  & & & \\ \hline Personalisation & The analysis can be adjusted to specific needs (with prompts). Contextual analysis. & A predefined test scope and a limited list of metrics. Test scope defined with a dashboard. The results cannot & [12; 15; 36] \\  & The results can be discussed. & & \\ \hline Limitations and weaknesses & The results may contain hallucinations, i.e., anticipated or projected results instead of actual results. & The results are often aggregated. & [15; 37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between auditing tools in the form of AI plugins and web applications. identifying problems with content accessibility. They concluded that LLMs could increase the number of detected WCAG incompatibilities. [PERSON] et al. [39] employed an LLM to analyse the sentiment behind user-generated content (UGC). They compared two language models, ChatGPT 3.5 and Claude 3, and three traditional NLP methods using two datasets with customer reviews. The results show that LLMs highly effectively extract information from large datasets. [PERSON] et al. [39] pointed out LLM's ability to streamline analytical processes, albeit under user supervision, necessary to ensure the reliability of results. [PERSON] and [PERSON] [5] proposed a new approach to web page generation with LLMs. It helps focus on the design rather than the (hypertext and programming) code. Prompt engineering is pivotal for this approach as it causes specific code modifications, resulting in the final page layout.