duo-difficulty-calibration

Put each user at the edge of what they can currently do — estimate item difficulty and user ability in one model, target a band rather than a floor, and use live signals to tell whether the model is honest. Covers joint item-user modelling, the familiar-to-new ratio, error rate read against completion rate, the roughly-half success check, fitted difficulty weights as a content brief, why users grind mastered material, opt-in hard modes priced higher, user-picked difficulty rungs, tail escalation, and training above real load. Use when asked how hard the next thing should be, what error rate means it is working, whether an adaptive system is calibrated, how much new versus familiar material, whether users should pick their own level or the system should infer it, or when an accuracy drop is actually progress. If the screen asks too much at once, use the attention-budget skill instead.

Version

2.0.0

License

MIT