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Assessment Engine

Adaptive testing powered by Item Response Theory

Every learner gets a different exam — one that's always measuring at the exact difficulty level where they provide the most information. The result: 40–60% fewer questions for the same measurement precision as a static 100-item test.

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The Algorithm

How the adaptive algorithm works

The engine implements the 3-Parameter Logistic (3PL) IRT model. Each item in your bank has three calibrated parameters: difficulty (b), discrimination (a), and pseudo-guessing (c). After each response, a Maximum Likelihood Estimate (MLE) updates the latent ability estimate (θ).

Response latency is folded in as a Bayesian prior — unusually fast correct answers reduce the weight of that response; unusually slow correct answers increase the information yield estimate. The next item is selected to maximize Fisher Information at the current θ.

3PL IRT Model Bayesian Estimation Fisher Information
Adaptive assessment engine diagram showing difficulty calibration curve adjusting in real time based on learner response signals
Platform Features

Everything your assessment program needs

Item Bank Management
Organize items by domain, difficulty tier, and cognitive level. Automatic parameter calibration from pilot response data.
Branching Logic
Define content exposure rules, maximum item use limits, and content balancing constraints across ability levels.
Real-Time Difficulty Calibration
Item parameters auto-update from live response data using concurrent calibration. Your bank improves with every administered test.
Full API Access
REST API with read/write access to sessions, items, and scores. Embed adaptive assessments in any platform — LMS, HRIS, or custom portal.
Get Started

See the engine adapt in real time

We'll walk you through a live demonstration with your own subject matter — watch the 3PL model branch in real time as we answer questions at varying ability levels.

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