Understanding and Improving Accessibility in AI-Generated Interfaces through Interactive Prompt Engineering Methods

Abstract

As interface designers increasingly adopt AI-powered design tools, ensuring accessibility compliance presents unique challenges for human-AI collaboration.Through a mixed-methods study combining systematic evaluation of 200 generated interfaces across five AI design tools and interviews with six professional designers with extensive accessibility experience, we investigated patterns in designer-AI collaboration for accessible interface design.While 72% of interfaces exhibited accessibility violations, these were relatively minor (M = 1.2, SD = 0.4).Our analysis revealed distinct interaction patterns between designers and AI tools: iterative refinement (42% of cases) where designers progressively improved accessibility through dialogue, requirement prioritization between accessibility compliance and visual design, design consistency during targeted modifications.Component-level iterations had a median of 3 rounds, with average values suggesting similar behavior (M = 3.2, SD = 0.8), each targeting specific accessibility issues.Notably, the difference between control and accessibility-oriented prompts was minimal (Δ = 0.2), suggesting that successful accessibility implementation requires active designer engagement beyond initial prompting.We contribute empirical insights into designer-AI dialogue patterns and provide implications for developing more effective accessibilityfocused AI design tools.

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