Mini Artifacts
An experiment in giving AI a spatial layer — can conversation-aware cards that spawn, evolve, and dissolve reduce cognitive load in planning tasks?
TL;DR
Built an interactive research prototype that adds a spatial card layer to AI chat — living cards that persist and mutate, ephemeral cards that appear and dissolve — testing whether AI-managed visual context reduces cognitive load in multi-step planning.
Overview
Role
Design Researcher & Prototyper
Domain
AI / Conversational UX
Stack
React 19, TypeScript, Tailwind, Vite
01 — Observation
The linear stream problem
I noticed a pattern across ChatGPT, Claude, and Gemini: when users plan complex, multi-step tasks through chat, structured information — checklists, budgets, timelines — gets buried in the message stream.
The gap: AI has no mechanism to proactively manage structured, evolving visual context tied to the conversation lifecycle.
02 — Hypothesis
A spatial layer for AI
What if we gave the AI a second output channel? Not just text in a stream, but cards it can spawn, mutate, and dismiss — living alongside the chat in a three-column layout.
03 — Experiment
Two modes, one system
The key insight during prototyping: not all context has the same lifespan. A budget tracker should persist and evolve. A definition should appear, help, and get out of the way. This led to a dual-card architecture.
04 — Test Scenarios
Six scenarios
To test whether the pattern generalizes beyond a single use case, I built six scripted demos across different domains. Each spawns the same card types with domain-specific content.
Same card system, six different domains. If the pattern only worked for trip planning, it would be a feature. Working across all six suggests it's a generalizable interaction model.
05 — Design Rationale
Why these choices
Each design decision in the prototype was made to isolate a specific variable in the experiment.
06 — Validation
How to prove it
A prototype proves feasibility, not value. This is the evaluation framework I'd use to determine whether the spatial card model actually helps — or just looks interesting.
Core hypothesis: AI-managed spatial cards reduce cognitive load and improve task outcomes in multi-step planning conversations, compared to a standard linear chat.
A/B test structure
What to measure
Kill criteria
07 — Status
Where this stands
The prototype is built and functional across six domains. It proves the interaction model is technically viable and generalizable. What it hasn't proven yet: whether users actually benefit from it. The next step is running the validation framework above with real participants to find out if this is a useful pattern — or just an interesting one.