# 01 - Adaptive Learning Algorithms: From Theory to Production

Implementing adaptive learning algorithms: IRT, knowledge tracing, personalized paths and A/B testing in production.

## What you'll learn

- IRT provides a solid mathematical foundation for item difficulty calibration and Computerized Adaptive Testing
- BKT is interpretable and fast; DKT is more accurate for complex multi-KC interactions
- Separate your online serving path (Redis + FastAPI) from offline training (batch jobs)
- A/B test with student-level hashing to prevent contamination between variants
- Monitor for concept drift; curricula and student populations change over academic cycles

*This article is part of the **EdTech** series on federicocalo.dev.*

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## Read the full article

The complete article (28 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [01 - Adaptive Learning Algorithms: From Theory to Production](https://federicocalo.dev/en/blog/adaptive-learning-algorithms-theory-to-production)**

`https://federicocalo.dev/en/blog/adaptive-learning-algorithms-theory-to-production`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*