## 6 Conclusions
#### Practical Implications and Future Research  
According to the declared specifications, the tested AI plugins conduct actual analyses, such as performance measurements or verification of compliance with Web Content Accessibility Guidelines. In fact, the results are a mixture of facts and projections (prognoses), resulting in a set of AI hallucinations. For a non-expert user who is also unaware that the audit result might be false, the'measurement and reporting' operation seems convincing. One has to ask about technical intricacies to discover the true nature of the reported results because the AI claims an audit has indeed been conducted. The problem is due to the design of LLMs, which prioritise responding to prompts, leading to projected results. Therefore, verifying AI results with independent test tools is essential. Consequently, one has to know how LLMs work to guide the tool and make full use of its potential. Language models should be improved to clearly communicate what the reported data are and avoid asserting the performance of activities that were, in fact, not performed. The performance and reliability of audit AI plugins operating in LLM ecosystems have to be further investigated, especially regarding AI hallucinations. Future research could focus on expanding the capabilities of integrating LLMs with third-party analytical systems (tools) for exploring website source code or performing automated laboratory measurements. The analyses suggest that the anomalies concerning how AI plugins report quality audit results are recurring and can occur regardless of the specificity of the website. Nevertheless, it would be valuable to include various types of websites in future research to gain insight into the variability of AI hallucination intensity depending on the context and type of the website and its design. Further research on reporting results is necessary so that LLM communication reflects reality instead of voicing hallucinations, as this could promote user trust in the technology. It could be a promising path to design hybrid testing methods that would combine the analytical capabilities of language models with actual data from dedicated measurement tools. Alternatively, one could functionally enable language models to perform independent measurements. Furthermore, it is pivotal to develop methods for validating AI results by comparing them with reference data, for example. Regarding the presented results, it would be useful to design automated verification systems for AI responses that would minimise the number of manual control operations, improving audit efficiency. Note that these systems would be overt so that the user could enable them in CUI/GUI as opposed to systems in the 'black box' of AI algorithms. Possibly, users could trust AI more if they could use such 'AI validators'. Co-financed by the Minister of Science under the 'Regional Initiative of Excellence' programme. Agreement No. RID/SP/0039/2024/01. Subsidised amount: PLN 6,187,000.00. Project period: 2024-2027. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the author. The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript: