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Customizable Bayesian adaptive testing with Python - The adaptivetesting package
Jonas Engicht1, R Maximilian Bee2, Tobias Koch2
1Institute of Psychology, Friedrich-Schiller University Jena, Am Steiger 3, Haus 1, 07743, Jena, Germany. jonas-engicht@uni-jena.de.
Behavior Research Methods
|July 24, 2026
Summary
This study presents a new Python package for creating adaptive tests. It simplifies test development and deployment, enabling easier transitions from simulation to real-world application.
Area of Science:
- Psychometrics
- Computer Science
- Software Engineering
Background:
- Computerized adaptive testing (CAT) is a powerful tool for ability estimation.
- Existing Python packages for CAT lack sophistication and integration capabilities.
- Bridging the gap between CAT simulation and practical implementation is crucial.
Purpose of the Study:
- Introduce an open-source Python package for simplified, customizable CAT.
- Facilitate seamless integration of CAT into experimental software.
- Enhance the flexibility and extensibility of Bayesian ability estimation in CAT.
Main Methods:
- Developed an open-source Python package named 'adaptivetesting'.
- Utilized object-oriented programming principles for extensibility.
- Provided a dedicated API for integration with experiment software.
- Enabled customization of Bayesian estimators, including custom priors.
Main Results:
- The 'adaptivetesting' package simplifies the creation and deployment of CAT.
- It allows for easy transition from simulated to real-world adaptive testing without major code rewrites.
- The package supports customization of Bayesian methods for ability estimation.
- It is accessible via pip and conda, and can be used within RStudio.
Conclusions:
- The 'adaptivetesting' package offers a robust and flexible solution for CAT in Python.
- It lowers the barrier for researchers and developers to implement sophisticated adaptive testing.
- The package promotes wider adoption of advanced psychometric methods in research and practice.
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