# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 1 Introduction
### Proposed Solution and Contributions  
In this paper, we introduce AccessiDashboard, an IoT dashboard platform that integrates a modern web application framework with robust accessibility fundamentals and AI-driven data descriptions. The main contributions of this work are as follows:  
* **Accessibility-First Semantic Design:** AccessiDashboard uses semantic HTML5 markup and WAI-ARIA (Web Accessibility Initiative--Accessible Rich Internet Applications) [13] attributes to ensure that screen readers can interpret the page structure and content intuitively. The front-end interface is organized with landmarks (e.g., <header>, <main>, <section> regions), headings, and labels for real-time sensor elements. This structure provides a meaningful reading order and enables efficient keyboard navigation for users who cannot rely on visual cues.
* **AI-Generated Long Descriptions:** Moving beyond simple alt text, the system leverages an LLM to produce extended textual descriptions of charts and complex graphics. These long descriptions detail trends, anomalies, and key numerical insights that would otherwise be conveyed only visually. The AI-generated narratives serve as an alternative interface modality, catering to users who cannot interpret the visuals. Importantly, these descriptions are context-aware and can include relevant domain knowledge (e.g., noting that a temperature reading is within normal range for a given context).
* **Multi-Modal Data Representation:** AccessiDashboard provides multiple synchronized representations of the sensor data. In addition to the conventional chart-based view, it offers a text-first interface comprising structured data tables, bullet-point summaries of sensor status, and AI-generated analytical paragraphs. Users can switch between these modes to suit their preferences or assistive technology needs. This multi-modal approach benefits not only blind users but also those who may prefer textual data (for example, users of screen magnifiers or braille displays, or situations where visual attention is limited).
* **Scalable Architecture with Ethical AI Integration:** The platform's back-end is built with the Laravel PHP framework [14] following an MVC (Model-View-Controller) [15] design, which allows clean separation of data, logic, and presentation. The system integrates with external IoT data sources through APIs (Application Programming Interfaces) [16] for device data ingestion, and uses a database to store sensor readings and corresponding AI-generated descriptions. A privacy-by-design [17] paradigm is adopted for the AI components: user data is handled in compliance with GDPR guidelines [18], with controls to prevent sensitive information from being sent to external AI services and logs to audit the AI's outputs. The design also considers scalability (able to handle many sensors and frequent updates) and security (secure transmission of data and user authentication).  
To validate our approach, we conducted an initial evaluation through manual testing and the use of automated tools to verify that the dashboard meets or exceeds WCAG 2.1 AA accessibility criteria [19]. We also compare AccessiDashboard qualitatively against conventional IoT dashboards to highlight improvements. The remainder of this paper is organized as follows: Section 2 reviews related work in accessible dashboards and AI-driven accessibility. Section 3 describes the system design and architecture of AccessiDashboard, including its core components and data flow. Section 4 details the implementation, demonstrating how the platform realizes accessible UI and AI-enhanced descriptions (with illustrative examples). Section 5 discusses the implications of our design, evaluation findings, and how AccessiDashboard fits into broader trends, as well as limitations. Finally, Section 6 concludes the paper and outlines directions for future work.