From Algorithmic Ethics to Academic Honesty: University Educators' Perspectives on the Challenges of Generative Artificial Intelligence

The relevance of addressing the challenges and risks of using generative artificial intelligence (GAI) in higher education is linked to the necessity of moving away from a technocentric paradigm of analyzing the ethical aspects of the functioning of machine learning models themselves (algorithms), which are primarily reduced to issues of developer responsibility, security, confidentiality, and the reliability of the created content. Instead, it requires a transition to an anthropocentric paradigm of "academic honesty," at the center of which lies the ethical aspect of the behavior of the individual interacting with these algorithms.

Article Objective: To analyze the subjective position of a key agent in higher education – instructors

– and their perception of ethical challenges associated with the use of Generative Artifi Intelligence (GAI).

Scientific Novelty: The scientific novelty of this article lies in the interpretation of ethical risks of GAI use in higher education as institutionally organized and requiring a systemic approach to their management, as well as in the empirical research findings. These findings confirmed a high level of instructor reflection on the nature and causes of ethical risks of GAI use in higher education, the importance of unified data protection, and adherence to academic integrity principles, while leaving room for instructors' pedagogical freedom within these standards.

1. An Y., Yu J.H., James S. Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration // International Journal of Educational Technology in Higher Education. 2025. Vol. 22. Art. 10. https://doi. org/10.1186/s41239-025-00507-3.

2. Гидденс Э. Риск, судьба, безопасность // Thesis. 1994. Вып 5. C. 107–134.

3. Шайбакова Л.Ф., Молокова Е.Л. Теоретические подходы к определению понятия институционального риска системы высшего профессионального образования // Известия УрГЭУ. 2013. № 5. (49). С. 119–124.

4. Marín Y.R., Caro O.C., Rituay A.M.C. et al. Ethical Challenges Associated with the Use of Artificial Intelligence in University Education // Journal of Academic Ethics. 2025. Vol. 23. P. 2443–2467. https://doi.org/10.1007/s10805-025-09660-w

5. Бурганова Л.А., Мягков Г.П., Юрьева О.В. Университет 4.0: этические проблемы цифровизации высшего образования // Российское общество сегодня: ценности, институты, процессы. Материалы Всеросс. науч. конф. СПб., 2023. С. 781–783.

6. Бурганова Л.А., Мягков Г.П., Юрьева О.В. Отношение университетских преподавателей к искусственному интеллекту // Вестник экономики, права и социологии. 2025. № 4. С. 220–225.

7. Полани М. Личностное знание. На пути к посткритической философии. М.: «Прогресс», 1985. 344 с.