DESIGN GOAL
An intuitive, low learning cost AI co-pilot for architects.
Designed for architects at their early concept stage
3 Core features for MVP
Selecting architecture location
DESIGN STRATEGY #1
System knowability and user control
Architecture data input
AI models are generated based on data requirements. In architecture, relevant data includes programs, use types, sites, etc. that impact the outlook of architecture. In a study (an architecture building concept) creation process, architects need to input the data series for the machine.
In a complex data input process, I designed clear indications of data input progress and completion status to inform users of the steps and allow them to switch between different steps and make edits easily. I did not choose to pursue a design that only suggests progress with progress dots.
Data input progress only indicated with progress dots.
Data input progress controlled by a side bar that supports edits back and forth.
The final solution offers a clear guidance on data input steps, progress, and offers flexible switches between tabs.
DESIGN STRATEGY #2
AI co-pilot operability
Supporting synchronous data tracking and AI edits
DESIGN STRATEGY #3
Information recovery and error handling
Restoring design changes
Editing model inputs
The Design Canvas
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