Sounds wild but doable if you think of the script as a finite-state machine. Each scene is a state, and the audience feedback – maybe a quick sentiment score from a micro‑camera or a tap‑count on a screen – drives transitions. Build a tiny graph where nodes are scenes, edges are weighted by how likely a given emotional cue will trigger the next scene. Then run a Markov‑style selector: at every cue, pick the neighbor with the highest weight that still keeps the story line intact. You can keep coherence by tagging each scene with a “theme tag” and only allowing transitions that keep the tag in the same family, or by enforcing a rule that you can’t skip more than two scenes ahead. The code can be a simple loop that waits for the cue, updates a current state variable, and emits the next block of code to render. If you want to get fancy, add a small neural net that learns the weights from audience reactions over time. Just remember to keep the state machine small enough that the audience doesn’t see the logic – they should feel like the story is just flowing, not jumping through a menu.

Sure thing – I’d write a unit‑test harness that mocks the audience sensor. Feed it a pre‑recorded stream of “emotional values” and step through the state machine. Each test case asserts that the chosen next scene is one of the allowed neighbors and that the theme tag hasn’t drifted too far. Then run the whole thing in a sandbox with a tiny UI that visualises the graph so you can see if any edge looks like a rogue road. If you want a quick sanity check, just spin the code with a random walk through the edges and watch the generated script – if it still reads like a movie, you’re probably fine. If it starts talking about turtles or something, you know you need to tighten the weights.