Design Behaviour and Interface Consistency in Generative No-Code Tools: A Systematic Literature Review

Generative no-code development tools enable users to create applications directly from natural-language prompts, shifting interface design from manual construction to AI-mediated generation. However, identical prompts frequently produce substantially different user interface (UI) outcomes across tools and even across repeated executions within the same tool. This paper presents a systematic literature review examining how generative no-code systems make design and aesthetic decisions with respect to layout structure, visual consistency, usability, accessibility, and reproducibility. Twenty peer-reviewed studies (2021–2025) were analyzed following a structured review protocol. Existing research predominantly evaluates usability and accessibility in isolation while providing limited insight into aesthetic coherence, design variability, and prompt-to-output stability. Across studies, generative tools exhibit implicit design priors and stochastic behavior that lead to inconsistent visual outcomes and partial misalignment with human-centered design principles. These findings indicate that generative no-code tools do not act as deterministic translators of user intent but instead introduce their own stylistic tendencies. The paper identifies critical evaluation gaps and outlines requirements for future systems, including reproducible generation, transparent design reasoning, and user-directed control, to support reliable and predictable interface development.