# Optional: Notify admin for review if adminReviewEnabled: sendToReviewQueue(sensorID, description)
## 5 Discussion
Additionally, AccessiDashboard employs prompt-engineering strategies to limit the scope of AI outputs. Descriptions are generated based strictly on bounded, factual sensor data--such as temperature, humidity, AQI, or energy use--without speculative forecasting or diagnostic assertions. This constraint reduces the risk of erroneous interpretations while preserving the utility of the generated narratives. Where data sensitivity is higher (e.g., smart healthcare or safety-critical monitoring), a human-in-the-loop model is recommended. This allows organizations to preview and validate AI-generated content before display. Furthermore, fallback mechanisms are implemented to either suppress uncertain outputs or revert to structured templates when necessary. Overall, we position the AI-enhanced layer of AccessiDashboard as a supportive tool in the broader accessibility pipeline--not a replacement for expert interpretation or critical alerts. Its aim is to reduce informational barriers for users with visual or cognitive impairments, but with careful design, transparency, and ethical safeguards in place. Future research should continue to refine these methods and explore how trust, explainability, and inclusivity can be balanced in adaptive, intelligent systems. A preliminary audit of AccessiDashboard combined expert screen-reader walk-throughs with automated tools such as Lighthhouse [55] and WAVE, confirming that the current prototype satisfies WCAG 2.1 AA criteria. Key accessibility features were validated, including logically ordered headings, semantically meaningful landmarks, and full keyboard navigability. Despite these encouraging results, the current evaluation has limitations. The initial testing relied on a small sample of accessibility experts and focused predominantly on English-language screen reader environments. Broader linguistic and cultural accessibility aspects remain to be explored. Automated accessibility tools were valuable in detecting structural issues such as contrast errors or ARIA misconfigurations, and they helped guide iterative improvements. However, we agree with the common observation that such tools--though useful--are not substitutes for direct feedback from users with disabilities. They cannot assess contextual comprehension, navigational intuitiveness, or cognitive load. As such, the current demonstrator should be seen as a strong technical foundation rather than a complete solution. The integration of a large language model proved central to reducing cognitive load [56]. Instead of forcing users to infer patterns from raw tables, the system now offers concise stories of what the data are doing, thereby turning numeric trends into immediately understandable insights. By closely aligning with real sensor data, the system reduces the likelihood of generating inaccurate or misleading information. Because descriptions are generated automatically, expanding the dashboard to dozens or hundreds of sensors no longer multiplies authoring effort, making accessibility a built-in property rather than an afterthought. Real-world scenarios make the benefits tangible. In a smart-building context a facilities manager can understand energy trends without sight; in an industrial plant a supervisor detects abnormal vibration early and schedules maintenance; in remote healthcare a physician reviewing wearable data hears a clear synthesis of heart-rate patterns; and in a smart-city air-quality program environmental officers track pollution shifts across districts. In each setting, inclusive access means that professionals who rely on assistive technologies can act on time-critical information as independently and confidently as their sighted peers. These outcomes align with broader movements toward AI for social good, forthcoming accessibility regulations such as the European Accessibility Act, and an emerging design paradigm that emphasizes multi-modal, keyboard-accessible interfaces.