Web accessibility is essential for inclusive digital experiences, yet the accessibility of LLM-generated code remains underexplored. This paper presents an empirical study comparing the accessibility of web code generated by GPT-4o, Qwen2.5-Coder-32B-Instruct-AWQ, and Gemini-3-Flash against human-written code. Results show that LLMs often produce more accessible code, especially for basic features like color contrast and alternative text, but struggle with complex issues such as ARIA attributes. We also assess advanced prompting strategies (Zero-Shot, Few-Shot, Self-Criticism), finding they offer some gains but are limited. To address these gaps, we introduce FeedA11y , a feedback-driven ReAct-based approach that demonstrates the potential of incorporating accessibility evaluation results into the code generation process. Our work highlights the promise of LLMs for accessible code generation and emphasizes the need for feedback-based techniques to address persistent challenges. We provide the source code and datasets that were used in our experiments in the companion website 15.
Suh et al. (Fri,) studied this question.