# Model-Driven UI

DS256. Future / AI: Architecture. Page: https://ui.codeatlas.com.br/atlas/model-driven-ui/. Index: https://ui.codeatlas.com.br/llms/atlas.txt

Generation of forms, states and components from data models.

- Status in 2026: Emerging. Appearing in practice, not yet settled.
- Trend 2027 to 2030: Rising (an editorial projection, not a forecast).
- The term: Known technical term or standard.

## Traits

- schemas
- forms
- rules

Typical settings: ERP, Internal platforms.

## When to use it, and when not

Consider it when the task and motivation match: vertical application engineers. Typical settings: erp, internal platforms.

Be careful when the risk below would hurt the main task: generated UI becomes generic and inflexible.

Audience (a hypothesis about motivation and task, never measured, not a statement about any group): Vertical application engineers.

## UX risk and how to reduce it

- Risk: Generated UI becomes generic and inflexible.
- Mitigation and accessibility: Overrides and testing with users.
- How to check: Test with realistic scenarios: task success, accuracy, reversal, auditability, understanding of what the system does on its own, and a human's ability to step in.

## Evidence

Emerging: exploratory work, with no causal validation for this style.

The sources support the principles that apply to this term. None shows that this style works better or is preferred.

- [E16] Aljabri, Hijazi, Al-Salamin, Khemaja (2026). AI-MDD Approaches for Generating Adaptive and Intelligent UIs: A Systematic Literature Review. https://doi.org/10.20965/jaciii.2026.p1620 (Supported). Does not support: That adaptive generated UIs perform better for users. It maps the literature; the abstract read here does not report user outcomes.
- [E21] Romero, Wiese, Balancieiri and others (2026). Usable but Conventional: An Empirical Study on the UX of AI-Generated Interface Prototypes. https://arxiv.org/abs/2605.15124 (Preliminary). Does not support: A general verdict on AI-generated interfaces: one survey, particular prototypes and tools, and a preprint.

## Limits

- The atlas is a broad, living catalogue, not every style that exists. No authority enumerates them, and the classes (movement, aesthetic, design system, layout, pattern, principle) overlap.
- The 1 to 5 scores are relative editorial judgements. They are not results of a study and not a measure of quality.
- An audience is a hypothesis about motivation, task and context. It was never measured and says nothing about any age, gender or other group.
- The sources support underlying principles (visual complexity, legibility, grouping, accessibility, platform conventions). None shows that a particular style works better or is preferred.
- The 2027 to 2030 trend is an editorial projection, not a forecast.
- Terms in the Future / AI category rest on exploratory work, some of it preprints, with no causal validation.

Last reviewed 2026-10-25.

## Heuristic scales

Relative editorial judgements from 1 to 5, not results of a study.

- Visual complexity: 2 / 5 (heuristic)
- Expressiveness: 2 / 5 (heuristic)
- Information density: 5 / 5 (heuristic)

## In our library

No design-style recipe implements this term yet; build it from `--ca-*` tokens.

## Prompt for your AI

```text
Design this interface in the "Model-Driven UI" direction (future / ai, architecture): Generation of forms, states and components from data models.
Visual and interaction traits to use: schemas, forms, rules.
Main UX risk: Generated UI becomes generic and inflexible. Mitigate it: Overrides and testing with users.
Keep it accessible to WCAG 2.2 AA: contrast, visible focus, keyboard operation, 24 px minimum targets, reflow at 320 CSS px and reduced motion.
Keep the content, hierarchy and behavior the same as a plain baseline so the two can be compared. Do not claim the style performs better; the choice should be validated with users.
```