# Optional: Notify admin for review if adminReviewEnabled: sendToReviewQueue(sensorID, description)
## 4 Implementation Details
The system's accessible foundation, including semantic HTML5, ARIA roles, and appropriate structural markup, is automatically integrated into all newly added sensor widgets. Moreover, when AI-generated views are introduced, administrators serve as a critical human-in-the-loop component, testing not only the accessibility of the content but also verifying it for potential biases or inappropriate outputs. These administrators are not required to be accessibility experts; rather, they are supported by a lightweight validation workflow that includes the use of tools such as the WAVE Accessibility Evaluation Tool [51] (browser plugin). A simple scan with WAVE can reveal missing labels, incorrect heading hierarchies, or color contrast issues, enabling timely correction, as shown in Figure 7.  
To further support accessibility, the layout of each dashboard page, developed with embedded ARIA and semantic accessibility features, is transmitted to the AI model as a structural blueprint. This blueprint guides the AI in generating content that adheres to the predefined accessible layout, resulting in pages that are both semantically correct and fully accessible when rendered in the browser. This approach not only reduces the risk of non-compliant output but also ensures consistency and usability across both human- and AI-generated content. In summary, the implementation shows that an IoT dashboard can be made accessible without sacrificing functionality. By toggling content and using AI to supplement data visualization with data narration, AccessiDashboard provides a versatile interface. Below is a detailed comparison of AccessiDashboard, Azimuth, and Grafana [52], focusing on key accessibility features for IoT dashboards. Table 1 illustrates how the proposed solution, AccessiDashboard, improves upon conventional systems in several critical areas, including support for real-time accessible data, AI-generated descriptions, WCAG compliance, multimodal output, and human-in-the-loop validation. Unlike traditional dashboards, AccessiDashboard offers comprehensive text descriptions and adheres to accessible design paradigms. Its integration of AI-generated content is a novel feature that enables timely, detailed descriptions without requiring constant manual input. This comparative overview highlights AccessiDashboard's potential to serve as a more inclusive and effective tool for IoT data monitoring. Figure 7: Screenshot showing a dashboard page scanned with the WAVE plugin, highlighting accessibility structure and issues. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \begin{tabular}{c} **AccessiDashboard** \\ **(AI-Enhanced IoT Dashboard)** \\ \end{tabular}  & \ \begin{tabular}{c} **Azimuth** \\ **(Accessible Dashboard Prototype)** \\ \end{tabular}  & \begin{tabular}{c} **Grafana** \\ **(General Dashboard Platform)** \\ \end{tabular}  \\ \hline
**Dashboards** & \begin{tabular}{c} **Yes**. Designed for real-time IoT \\ sensor monitoring with \\ accessibility in mind. It delivers \\ live updates to screen reader \\ users by generating dynamic \\ textual summaries as data \\ streams in, ensuring blind users \\ get insights in real time. The \\ system uses ARIA live regions so \\ that changes (e.g., new sensor \\ readings) are announced. \\ \end{tabular}  & \ \begin{tabular}{c} **Yes** (dynamic updates supported). \\ Azimuth generates dashboards that \\ can be actively explored and updated. \\ This allows users to monitor changes \\ or apply filters in a dashboard and be \\ informed of updates without visual \\ cues. However, Azimuth was \\ demonstrated with user-driven data \\ updates (like filters) rather than \\ autonomous IoT sensor streams. \\ \end{tabular}  &