MEASURING STUDENTS' INFORMATION SECURITY COMPETENCE IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE: CRITERIA, INDICATORS AND RELIABILITY OF THE DIAGNOSTIC TOOLKIT

The rapid integration of artificial intelligence (AI) into higher education has created new opportunities for learning while increasing the complexity of information security risks faced by university students. These risks include phishing, identity theft, unauthorized disclosure of personal data, manipulated digital content, and the uncritical use of AI-generated information. Consequently, assessing students’ information security competence requires an approach that extends beyond theoretical knowledge and incorporates practical skills, risk assessment, responsible digital behaviour, and the critical evaluation of AI-generated outputs. This article proposes a multidimensional diagnostic assessment framework for measuring students’ information security competence in AI-supported educational environments. The framework comprises five criteria: cognitive, operational, analytical, behavioural, and ethical-legal. For each criterion, observable indicators and appropriate assessment methods are specified. The article also outlines procedures for evaluating the reliability and validity of the proposed diagnostic instruments, including internal consistency analysis, test–retest reliability, expert evaluation, and construct validation. The framework is informed by established digital competence and AI competence frameworks, as well as recognized principles of educational measurement. Since the proposed model has not been empirically validated within the scope of this article, its psychometric properties are presented as matters for future investigation rather than established findings. The proposed approach provides a methodological basis for developing evidence-based assessment instruments for higher education.