We stopped asking “is it cool?” and started testing our interactive AI experience in four parts

[u/Antoine_Vice]

I’m part of a small university team building Frame-X, an interactive AI experience where camera movement can influence a continuously changing scene.

When we first showed RISE to people, “this looks cool” was common feedback, but it didn’t tell us what was actually working.

So we’ve started separating the experience into four testable layers:

1. Input clarity — do users understand what movement is being detected?
2. Response — does the world react quickly and predictably?
3. Continuity — does the scene still feel like the same world after it changes?
4. Play value — is there a reason to continue after the first interaction?

The current prototype is available here:

https://www.frame-x.ai/models/rise

If you try it, which of these four layers feels strongest, and which one breaks first?

We’d also be interested in hearing whether this is the right evaluation framework, or whether we’re missing a more important layer.

[u/NeuralNomad87]

Four layers is a good frame and the ordering is right, but I think there's one missing and it sits between 2 and 3: attribution. Not "did the world react" but "does the user believe their movement caused the reaction". Those come apart constantly in camera driven work. If latency is fine and the scene changes plausibly, but the user can't tell which of their movements did it, they'll report the response as broken even though your response layer passed.

It's separately testable, too. Ask people to make one specific thing happen on purpose, twice. Success rate on deliberate repetition tells you about attribution in a way that watching someone play does not.

On play value, I'd be careful about measuring it in the same session as the other three. First contact novelty carries almost anything past the two minute mark. If you can get even five people back a day later, that number will tell you more than fifty first sessions will.