Abstract
Adaptive testing is one of the most effective approaches to digital knowledge assessment, providing personalization through the automated selection of test items tailored to the examinee’s proficiency level. The key components of such testing include: a bank of scaled test items, an adaptation algorithm, and specialized software. Developing a high-quality item bank requires preliminary psychometric analysis to evaluate their suitability for use in adaptive systems.This article presents an empirical analysis of a set of physics test items using the Rasch model. The study involved piloting the items on a representative sample of students, followed by scaling using the Winsteps software. For each item, difficulty parameters, model-fit indices, and correlation characteristics were determined. Items that did not meet the requirements of adaptive testing were identified and excluded from the final bank. As a result, a set of items with stable statistical properties was formed, suitable for further use in computerized adaptive knowledge assessment systems.The findings confirm the feasibility of integrating the developed item bank into educational information systems and digital platforms. Future publications will present real-time adaptive testing algorithms and the development of software for automated test generation based on scaled parameters. This study lays the groundwork for creating effective digital tools for assessing learning outcomes.