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## 6 Conclusions and Future Work  
This paper presented AccessiDashboard, a novel IoT dashboard platform that integrates accessible design principles with AI-generated descriptions to support visually impaired users. We demonstrated that, through semantic HTML5, WAI-ARIA annotations, and multiple modes of data representation, an IoT dashboard can be made perceivable and navigable to screen reader users without compromising functionality for others. The incorporation of LLM-based long descriptions provides an on-demand \"narrative layer\" to the typically visual domain of sensor data, offering insights that were previously hard to attain non-visually.  
Our comparative analysis indicates that AccessiDashboard could serve as a blueprint for inclusive dashboard design. By addressing both the technical aspects (real-time data handling, AI integration) and the user experience aspects (clear structure, meaningful text alternatives), we move toward closing the accessibility gap in IoT applications. Early evaluations suggest that the approach is effective in making complex data more understandable to blind users, although further user testing is required to quantify the benefits.  
For practitioners and researchers, this work underscores the feasibility of combining AI and accessibility: LLMs can indeed enhance user interfaces when guided appropriately. It also highlights the importance of going beyond compliance; true accessibility is achieved not just by meeting guidelines, but by ensuring the content delivered is as informative and usable as what sighted users get from a visual interface. We demonstrated an approach to achieving equivalent access by providing users with visual impairments a \"data storyteller\" that works alongside the data to enhance accessibility.  
Future work on AccessiDashboard explores several promising directions to enhance accessibility and generalizability. We plan to conduct comprehensive user studies involving individuals with visual impairments as well as sighted users, measuring performance metrics such as task completion time, data comprehension accuracy, cognitive load, and user satisfaction. To further support real-time interaction, we aim to implement audio and haptic alerts for critical sensor events, enhancing multimodal feedback. Integration with voice assistants is also under consideration, enabling users to access dashboard summaries via spoken commands.  
Building on this, we aim to adopt a more participatory design methodology. Future work will include both formative and summative user testing sessions with participants from diverse accessibility backgrounds, including individuals who are blind or neurodiverse. These studies will yield critical insights into real-world usability, enabling us to identify potential shortcomings and refine AccessiDashboard to ensure it serves a broader and more equitable user base.  
In conclusion, AccessiDashboard demonstrates that accessible IoT dashboards augmented with AI are not only possible but highly practical. By ensuring that no user is left behind in the rush toward data-driven smart environments, we contribute to a more inclusive future for the Internet of Things. We hope this work inspires further innovations at the intersection of AI, accessibility, and user interface design, ultimately leading to technology that serves all users equitably.  
Conceptualization, G.A.S. and L.S.; methodology, L.S. and N.E.-D.; resources, L.S.; writing--original draft preparation, G.A.S., L.S. and N.E.-D.; writing--review and editing, G.A.S., L.S. and N.E.-D.; supervision, G.A.S. and L.S.; funding acquisition, L.S. and N.E.-D. All authors have read and agreed to the published version of the manuscript.  
The study was carried out with the support of Transilvania University of Brasov through its institutional research resources; no dedicated grant or project number applies.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.