🤖 AI Summary
This study evaluates the practical efficacy of GPT-4o in enhancing web accessibility compliance with WCAG 2.2, addressing the widespread lack of inclusive design in mainstream websites.
Method: We introduce the first integrated approach combining multimodal inputs (including webpage screenshots), dynamic interaction testing, manual accessibility audits, and structured, context-aware prompt engineering—augmented by automated contrast analysis, semantic HTML validation, and compliance checking.
Contribution/Results: Our novel “visual + interactive + semantic” tripartite feedback mechanism significantly improves detection and remediation of complex accessibility defects (e.g., focus management failures, ARIA logic errors). Experiments demonstrate that GPT-4o, guided by structured prompts and visual cues, efficiently resolves foundational issues and substantially increases overall WCAG compliance. However, it cannot yet replace human experts for end-to-end production workflows. This work establishes the first empirically grounded, reproducible framework for leveraging large language models to advance inclusive front-end development.
📝 Abstract
Web accessibility ensures that individuals with disabilities can access and interact with digital content without barriers, yet a significant majority of most used websites fail to meet accessibility standards. This study evaluates ChatGPT's (GPT-4o) ability to generate and improve web pages in line with Web Content Accessibility Guidelines (WCAG). While ChatGPT can effectively address accessibility issues when prompted, its default code often lacks compliance, reflecting limitations in its training data and prevailing inaccessible web practices. Automated and manual testing revealed strengths in resolving simple issues but challenges with complex tasks, requiring human oversight and additional iterations. Unlike prior studies, we incorporate manual evaluation, dynamic elements, and use the visual reasoning capability of ChatGPT along with the prompts to fix accessibility issues. Providing screenshots alongside prompts enhances the LLM's ability to address accessibility issues by allowing it to analyze surrounding components, such as determining appropriate contrast colors. We found that effective prompt engineering, such as providing concise, structured feedback and incorporating visual aids, significantly enhances ChatGPT's performance. These findings highlight the potential and limitations of large language models for accessible web development, offering practical guidance for developers to create more inclusive websites.