MEASURING STUDENTS' INFORMATION SECURITY COMPETENCE IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE: CRITERIA, INDICATORS AND RELIABILITY OF THE DIAGNOSTIC TOOLKIT
Keywords:
artificial intelligence, information security competence, higher education, diagnostic assessment, assessment criteriaAbstract
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.
References
1. Загвязинский В.И., Атаханов Р. Методология и методы психолого-педагогического исследования. – М.: Академия, 2005. – 208 с.
2. Беспалько В.П. Слагаемые педагогической технологии. – М.: Педагогика, 1989. – 192 с.
3. Новиков Д.А. Статистические методы в педагогических исследованиях (типовые случаи). – М.: МЗ-Пресс, 2004. – 67 с.
4. Богатырева Ю.И. Подготовка будущих педагогов к обеспечению информационной безопасности школьников: дис. ... д-ра пед. наук. – Тула, 2014. – 405 с.
5. Привалов А.Н., Богатырева Ю.И. Модель формирования компетентности будущих педагогов в области информационной безопасности // Информатика и образование. – 2013. – № 5. – С. 45–49.
6. Hadlington L. Human factors in cybersecurity; examining the link between Internet addiction, impulsivity, attitudes towards cybersecurity, and risky cybersecurity behaviours // Heliyon. – 2017. – Vol. 3, № 7. – Article e00346.
7. Vuorikari R., Kluzer S., Punie Y. DigComp 2.2: The Digital Competence Framework for Citizens. – Luxembourg: Publications Office of the European Union, 2022. – 133 p.
8. Laupichler M.C., Aster A., Schirch J., Raupach T. Artificial intelligence literacy in higher and adult education: A scoping literature review // Computers and Education: Artificial Intelligence. – 2022. – Vol. 3. – Article 100101.
9. Ng D.T.K., Leung J.K.L., Chu S.K.W., Qiao M.S. Conceptualizing AI literacy: An exploratory review // Computers and Education: Artificial Intelligence. – 2021. – Vol. 2. – Article 100041.
10. O'zbekiston Respublikasining "Shaxsga doir ma'lumotlar to'g'risida"gi Qonuni. 2019-yil 2-iyul, O'RQ-547-son.
11. OWASP Top 10 for Large Language Model Applications. Version 1.1. – OWASP Foundation, 2023. – 33 p.
12. Tolipov O'.Q., Usmonboyeva M. Pedagogik texnologiyalarning tatbiqiy asoslari. – Toshkent: Fan, 2006. – 261 b.
