Can AI Really Boost UX Design? A Hands‑On Review of ChatGPT, Midjourney and Emerging Tools

ChatGPT has been in the public eye for a year and, as the most talked‑about AI product, its impact is evident. Alongside it, AI software for other domains continues to emerge.

Most users have experienced ChatGPT/Claude’s ability to expand ideas and optimize copy, as well as Midjourney/Stable Diffusion’s assistance with banner and icon design.

Key AI capabilities examined:

ChatGPT/Claude: research outlines, communication plans, user analysis, data analysis, competitor analysis, solution strategies, copy optimization.

Midjourney/Stable Diffusion: startup pages, status illustrations, banner design, icon design, IP imagery, logo design.

These capabilities have not yet been fully extended to core UX scenarios such as page and flow design.

We evaluated four distinct AI functions that do not overlap with the core abilities of ChatGPT/MJ/SD:

Text/sketch‑to‑prototype generation.

Screenshot‑to‑editable prototype.

Automatic design‑style generation.

Design‑system extraction and application.

Text/sketch‑to‑prototype – Tools like Uizard, CodeDesign.ai, Instant AI, and XiaoMo AI can generate prototypes from textual descriptions. Uizard and Instant AI produce high‑fidelity mobile prototypes, while CodeDesign.ai focuses on adaptable web layouts. In tests, both Uizard and Instant AI generated acceptable bilingual prototypes, but each missed some required elements and often produced flat, non‑interactive layouts.

Pros: Fast image generation, multiple design options, style keywords for varied outputs (Uizard).

Cons: Limited to framework generation; detailed elements require manual correction; difficulty handling specific module requirements.

Screenshot‑to‑editable prototype – Allows rapid conversion of existing screens into editable prototypes, useful for quick adaptations of competitor designs or when source files are unavailable.

Pros: Editable icons, text, colors; large creative space when combined with built‑in editors.

Cons: Limited applicability for designers; not a complete solution.

Automatic design‑style generation – By providing a style description, reference image, or URL, the tool can re‑style an existing prototype, similar to applying a mood board.

Pros: One‑click style application across all screens; supports Figma and Sketch imports.

Cons: Only a single style can be generated; limited control over individual module adjustments.

Design‑system extraction and application – Imports an image or webpage, extracts a design system, and applies it to a prototype, offering a middle ground for controllable results.

Pros: Ideal when a primary color palette is known; adjusting system colors updates all related modules.

Cons: Some tools (e.g., Visily) lack compatibility with Sketch/Figma imports; fixed color‑module mappings may require manual tweaks.

Overall, while AI tools can generate UX‑related assets, they function more like plugins than full‑featured design tools, and their integration into daily workflows remains limited.

Current mainstream AI excels at broad information synthesis (ChatGPT) or high‑quality visual generation (Midjourney/Stable Diffusion), but struggles with deep contextual understanding required for targeted UX solutions, often necessitating manual post‑editing.

To advance AI‑assisted UX design, we recommend focusing on two capabilities: design differentiation —identifying unique product advantages and user differences—and design judgment —evaluating multiple concepts to select the best fit, supported by continuous data‑driven feedback.

In summary, AI development is rapid, and designers must keep pace to explore new possibilities.