The design principles guiding my UX Assessment tool

What if AI could evaluate UX design as effectively as a senior UX designer?

I'm building the bridge between artificial and human design intelligence - a tool that doesn't just assess UX, but revolutionizes how we think about design evaluation.

PROGRESS SO FAR: 

I've completed the initial wireframes for the main assessment flow and defined the visual design system.

For the UI, I'm using a clean and professional mint green color palette that creates a calm, focused environment for assessment work.

The goal is to make the assessment process both professional and approachable.

BEHIND THE SCENES: 

Here's something I haven't shared publicly: I tested five different color palettes with designer friends before settling on this direction.

The results were surprising—designers consistently rated the vibrant mint green palette as more "trustworthy" and "professional" compared to muted options, challenging typical assumptions about enterprise software design.

DESIGN DEEP DIVE: CORE UX PRINCIPLES

The UX Skills Assessment tool will evaluate designs based on five fundamental principles:

1. Visual Hierarchy

   • Rationale: How elements are visually prioritized directly impacts user understanding

   • Evaluation approach: The AI will analyze size relationships, color contrast, positioning, and whitespace to determine if the visual hierarchy aligns with the information hierarchy

   • Common mistakes: Equal visual weight for all elements, competing focal points, buried primary actions

2. Accessibility

   • Rationale: Good design works for everyone, regardless of abilities

   • Evaluation approach: The tool will check color contrast ratios, text sizes, alt text presence, keyboard navigability, and other WCAG guidelines

   • Common mistakes: Insufficient contrast, relying solely on color to convey information, inaccessible interactive elements

3. Consistency

   • Rationale: Consistent interfaces reduce cognitive load and build user confidence

   • Evaluation approach: The AI will identify pattern adherence across components, spacing, typography, and interactions

   • Common mistakes: Inconsistent button styles, varying terminology for the same concept, unpredictable interaction patterns

4. Information Architecture

   • Rationale: How information is organized determines how easily users can find what they need

   • Evaluation approach: The tool will analyze content grouping, labeling clarity, navigation patterns, and information flow

   • Common mistakes: Illogical groupings, confusing navigation, buried important information

5. Usability

   • Rationale: The ultimate measure of design effectiveness is how easily users can accomplish their goals

   • Evaluation approach: The AI will evaluate against Nielsen's heuristics, analyzing system feedback, error prevention, user control, and recognition vs. recall

   • Common mistakes: Hidden system status, confusing error messages, lack of user control

The most interesting challenge in designing this tool is creating an interface that exemplifies these principles while evaluating them in others' work.

It's meta-design at its finest!

Reply directly to this email—I love hearing your perspectives!

Coming tomorrow: I'll share how I'm implementing the technical architecture to support AI-powered design analysis.

Let’s go!

John

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