A study assessed an LLM’s ability to generate accessible HTML code, revealing concerning shortcomings. The model demonstrated a superficial understanding of accessibility, often including unnecessary ARIA attributes and failing to address fundamental issues like form labelling and keyboard navigation. The findings underscore the need to improve training data to ensure AI-generated code adheres to accessibility best practices.
The text discusses the limitations of AI-generated code in terms of accessibility, highlighting issues like improper use of HTML elements, lack of error state management, and unnecessary JavaScript. It suggests that these limitations can be addressed by improving training data, fine-tuning models for accessibility, and integrating accessibility considerations into prompt engineering and IDE integrations. The author emphasizes the importance of prioritizing accessibility in AI-generated code to ensure a more inclusive web.