Sociocultural and technological aspects of the application of digital educational analytics in university education

Introduction. The article analyzes the sociocultural and technological aspects of implementing digital educational analytics, based on big data technologies, in the university education system. Its potential for monitoring academic performance, individualizing the learning process, and identifying students at risk is highlighted. Problems related to the loss of confidentiality of students' personal data, ethical contradictions, and differences in cultural perceptions of digital analytics emphasized. The purpose of the study is to develop methodological approaches to balancing technological innovations and data protection, taking into account the sociocultural context, which contributes to the formation of the concept of «digital trust».
Materials and methods. The methodology combines qualitative and quantitative approaches (surveys on the R. Likert scale, T-test, calculation of the ethical risk index and the probability of data leaks). The sample consisted of Russian universities with implemented analytics (stratified by courses and fields of study). Ethical principles were observed: anonymity, informed consent, data triangulation. Practices of implementing digital technologies such as differential privacy, blockchain, and machine learning were analyzed.
Results. The technological aspect of applying digital educational analytics demonstrates the effectiveness of data protection solutions but reveals compromises between security and data analytical accuracy. The differential privacy method, which adds "statistical noise" to the data, prevents individual identification while preserving aggregated utility, in compliance with legislative requirements. Blockchain technology ensures decentralized storage of academic records with cryptographic protection and access logging, reducing the likelihood of personalized data leaks and automating analytical processes. Machine learning enables local model training on university servers without transmitting raw data, exchanging only model parameters, thereby reducing data leak risks by 70%. The sociocultural aspect reveals the influence of cultural values on the perception of analytics: in collectivist societies, students more readily accept monitoring of emotions and behavior as a norm of trust in institutions, while in individualist ones, it is perceived as an invasion of autonomy, causing resistance and reducing motivation. The implementation of selective consent (choice of data for transmission) in university practice reduces student anxiety, as confirmed by the T-test with a downward trend. The proposed recommendations focus on the mandatory use of informed consent and ethical codes with sociocultural adaptation; the introduction of digital literacy courses for students and faculty; and the combination of technologies with «analysis-free» modules to preserve academic freedom.
Discussion and conclusions. The main contradiction in digital educational analytics is between its accuracy of analysis and predictions and the ethics of collecting, storing, and using students' personal data. Adding "noise" protects students but simultaneously reduces the predictive power of the analytical models used. Machine algorithms often reproduce existing social stereotypes, which is especially noticeable in multinational universities. The risk is that education is gradually dehumanized, and students may become "data objects," losing space for professional creativity and personal freedom. Digital educational analytics opens opportunities for personalization and optimization of learning but requires balancing innovations with humanistic values through federal auditing of algorithms and ethical standards adapted to local norms. Prospects for further developments may involve comparative analysis of regulations in different countries and continents; innovations in resource-saving cloud technologies with dynamic adaptation to ethics; and in-depth study of ethical factors across age, religious, and ethnic groups.

1. Avetisyan A. I. Artificial Intelligence in the Humanities. Threats and Opportunities. Vestnik Rossijskoj akademii nauk, 2024, vol. 94, no. 7, pp. 623-628, doi: 10.31857/S0869587324070028. (In Russ.)

2. Alekseev A. P., Alekseeva I. YU. Status of Ethical Codes in the Ethics of Artificial Intelligence. Informacionnoe obshchestvo, 2024, no. 4, pp. 43-49, doi: 10.52605/16059921_2024_04_43. (In Russ.)

3. Afanas'eva E. N. Artificial Intelligence Systems and Big Data in Law Enforcement Practice: Legal and Ethical Issues. Bol'shie dannye: materialy I Mezhdunarodnogo foruma, Novosibirsk, 16–18 noyabrya 2022 goda / otv. redaktor A. A. Makarcev, redkollegiya: T. N. Dudina [i dr.]. Novosibirsk, Novosibirskij gosudarstvennyj universitet ekonomiki i upravleniya «NINH» Publ., 2023. Pp. 169-176. (In Russ.)

4. Begishev I. R. Artificial Intelligence Technology: Global Development Experience. Baikal Research Journal, 2020, vol. 11, no. 3, p. 1, doi: 10.17150/2411-6262.2020.11(3).1. (In Russ.)

5. Gasparyan D. E., Turko D. S., Besschetnova E. V. Ethical Limitations of the Applicability of Artificial Intelligence: A Cultural-Philosophical Analysis. Voprosy filosofii, 2023, no. 9, pp. 93-102, doi: 10.21146/0042-8744-2023-9-93-102. (In Russ.)

6. ZHuravleva E. YU. Challenges of Big Data Technologies for Modern Social and Humanitarian Sciences. Voprosy filosofii, 2018, no. 9, pp. 50-59, doi: 10.31857/S004287440001353-3. (In Russ.)

7. Koval' E. A. Large Language Models and Moral Evil. Mir cheloveka: normativnoe izmerenie – 8.0. Anormal'noe: sbornik trudov konferencii, Saratov, 08–10 iyunya 2023 goda. Saratov, Saratovskaya gosudarstvennaya yuridicheskaya akademiya Publ., 2023. Pp. 256-262. (In Russ.)

8. Lesyuk M. I. Ethical Aspects of Neural Network Technologies in Science and Education. Iskusstvennye obshchestva, 2023, vol. 18, no. 3, doi: 10.18254/S207751800027043-6. (In Russ.)

9. Martynova M. D., Koval' E. A., ZHadunova N. V. Big Data as a Technological Challenge to Ethical Principles. Vestnik Moskovskogo universiteta. Seriya 7: Filosofiya, 2021, no. 4, pp. 84-101. (In Russ.)

10. Mejriyani M., Varganegara D. L., Andini V. Big Data, Machine Learning, Artificial Intelligence, and Blockchain in Corporate Management. Forsajt, 2023, vol. 17, no. 4, pp. 69-78, doi: 10.17323/2500-2597.2023.4.69.78. (In Russ.)

11. Muhachyova A. V. Digitalization in Foreign Countries: General Patterns and Features of Impact on Economic Development and the Social Sphere. Ekonomika. Nalogi. Pravo, 2025, vol. 18, no. 6, pp. 82-90, doi: 10.26794/1999-849X-2025-18-6-82-90. (In Russ.)

12. Paraskevov A. V., Uvarova A. G. Aspects of Big Data Applications. Filologicheskie i sociokul'turnye voprosy nauki i obrazovaniya: sbornik materialov VIII Mezhdunarodnoj nauchno-prakticheskoj ochno-zaochnoj konferencii, Krasnodar, 24 oktyabrya 2023 goda. Krasnodar, Kubanskij gosudarstvennyj tekhnologicheskij universitet Publ., 2023. Pp. 465-469. (In Russ.)

13. Pestunov A. I., Gintoft A. S., Krivetchenko O. V. Big Data as a Phenomenon: Causes and Consequences of the Emergence of Big Data. EKO, 2023, no. 9 (591), pp. 137-154, doi: 10.30680/ECO0131-7652-2023-9-137-154. (In Russ.)

14. Popov A. A. Technical and ethical standards in the field of big data. Gumanitarnye i politiko-pravovye issledovaniya, 2023, no. 4 (23), pp. 67-77, doi: 10.24411/2618-8120-2023-4-67-77. (In Russ.)

15. Revenko L. S., Revenko N. S. Artificial intelligence in Europe: state and regulation. Sovremennaya Evropa, 2025, no. 6 (134), pp. 189-201, doi: 10.7868/S3034599525060154. (In Russ.)

16. Revenko L. S., Revenko N. S. Artificial intelligence: modern approaches to multilateral regulation. Ekonomika. Nalogi. Pravo, 2025, vol. 18, no. 5, pp. 165-177, doi: 10.26794/1999-849X-2025-18-5-165-177. (In Russ.)

17. Smirnova A. I. Bias as a Problem of AI Algorithms: Ethical Aspects. Filosofiya i obshchestvo, 2023, no. 3 (108), pp. 118-126, doi: 10.30884/jfio/2023.03.07. (In Russ.)

18. Suslova T. I. Big Data Theory in the Socio-Humanitarian Context of Modern Knowledge. Sbornik tezisov po itogam Professorskogo foruma 2020 «Nacional'nye proekty i professorskoe soobshchestvo», Moskva, 16–19 noyabrya 2020 goda. Tom 1. Moscow, Obshcherossijskaya obshchestvennaya organizaciya «Rossijskoe professorskoe sobranie» Publ., 2020. Pp. 57-60. (In Russ.)

19. Upadh'yaya SH. Big Data and Official Statistics. Voprosy statistiki, 2019, vol. 26, no. 12, pp. 5-14, doi: 10.34023/2313-6383-2019-26-12-5-14. (In Russ.)

20. Haritonova YU. S., Savina V. S. Artificial Intelligence Technology and Law: Modern Challenges. Vestnik Permskogo universiteta. YUridicheskie nauki, 2020, no. 49, pp. 524-549, doi: 10.17072/1995-4190-2020-49-524-549. (In Russ.)

21. SHlyapnikov V. V. Big Data in Philosophy and Ethics Classes. Voprosy filosofii, 2025, no. 1, pp. 195-205, doi: 10.21146/0042-8744-2025-1-195-205. (In Russ.)

22. Eskindarov M. A., Gruzina YU. M., Harchilava H. P., Mel'nichuk M. V. The Role of Human Capital in the Digital Economy at the Institutional and Regional Levels. Ekonomika regiona, vol. 18, no. 4, pp. 1105-1120, doi: 10.17059/ekon.reg.2022-4-10. (In Russ.)

23. YArygina I. Z. Methods of subject analytics in higher education as factors in the formation of competencies of the teaching staff. Ekonomika. Nalogi. Pravo, 2024, vol. 17, no. 4, pp. 102-110, doi: 10.26794/1999-849X-2024-17-4-102-110. (In Russ.)

24. Alduais A., Qadhi S., Chaaban Y., Khraisheh M. The ETHICAL Protocol for Responsible Use of Generative AI for Research Purposes in Higher Education. AI Magazine, 2025, vol. 46, no. 4, p. e70047, doi: 10.1002/aaai.70047.

25. Alduais A., Qadhi S., Chaaban Y., Khraisheh M. Utilizing Generative AI Responsibly and Ethically for Research Purposes in Higher Education: A Policy Analysis. Serials Review, 2025, vol. 51 (3–4), pp. 120-170, doi: 10.1080/00987913.2025.2581429.

26. Al-Emran M., Al-Nuaimi M. N., Arpaci I., et al. Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms. Education and Information Technologies, 2023, no. 28, pp. 2727-2746, doi: 10.1007/s10639-022-11294-z.

27. Al-Emran M., Al-Sharafi M. A., Foroughi B., et al. Evaluating the influence of generative AI on students’ academic performance through the lenses of TPB and TTF using a hybrid SEM-ANN approach. Education and Information Technologies, 2025, vol. 30, no. 12, pp. 17557-17587, doi: 10.1007/s10639-025-13485-w.

28. Ayling Ja., Chapman A. Putting AI ethics to work: are the tools fit for purpose? AI and Ethics, 2022, vol. 2, pp. 405-429, doi: 10.1007/s43681-021-00084-x.

29. Dudin M. N., Afanasyev V. V., Voropaev M. V., Zasko V. N. Situation and problems of digitation of university management in Russia and in three Latin-American countries (Argentina, Chile, and Brazil). Formacion Universitaria, 2020, vol. 13, no. 6, pp. 61-76, doi: 10.4067/S0718-50062020000600061.

30. Eskindarov M. A., Gruzina Yu. M., Pulyaeva V. N., Ponomareva M. A. The Study on the Connection between Motivation for High-Performance Learning and the Level of Academic Performance among Students of Various Levels and Areas of Study. European Journal of Contemporary Education, 2023, vol. 12, no. 3, pp. 810-825, doi: 10.13187/ejced.2023.3.810.

31. Foroughi B., Griffiths M. D., Iranmanesh M., et al. Associations Between Instagram Addiction, Academic Performance, Social Anxiety, Depression, and Life Satisfaction Among University Students. International Journal of Mental Health and Addiction, 2022, no. 20, pp. 2221-2242, doi: 10.1007/s11469-021-00510-5.

32. Foroughi B., Senali M. G., Iranmanesh M., Khanfar A., Ghobakhloo M., Annamalai N., Naghmeh-Abbaspour B. Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 2024, vol. 40, no. 17, pp. 4501-4520, doi:10.1080/10447318.2023.2226495.

33. Jeon Ju., Kim L., Park Ja. The ethics of generative AI in social science research: A qualitative approach for institutionally grounded AI research ethics. Technology in Society, 2025, vol. 81, p. 102836, doi:10.1016/j.techsoc.2025.102836.

34. Johnston H., et al. Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 2024, vol. 20, no. 1, p. 2.

35. Kadaruddin K. Empowering Education through Generative AI: Innovative Instructional Strategies for Tomorrow’s Learners. International Journal of Business, Law, and Education, 2025, vol. 4, no. 2, pp. 618-625, doi: 10.56442/ijble.v4i2.215.

36. Kekez I., Lauwaert L., Begičević Ređep N. Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management. Technology in Society, 2025, vol. 81, no. 3, p. 102818, doi: 10.1016/j.techsoc.2025.102818.

37. Lee D., et al. The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 2024, vol. 6, no. 2, p. 100221.

38. Mihăescu M. Big Data and (the New?) Reality. American, British and Canadian Studies, 2023, vol. 41, no. 1, pp. 208-231, doi: 10.2478/abcsj-2023-0026.