Authors
- Tkhorikov Boris A. Doctor of Sciences in Economics, Professor
Annotation
The article examines the transformation of practical assignments in business education under the conditions of widespread use of generative artificial intelligence. Based
on a theoretical analysis and an online survey of teachers (n = 47) and students (n = 203) from Moscow universities, the study identifies the types of assignments that are most vulnerable to automation, as well as the factors that decrease or enhance their educational value. The findings demonstrate that tasks of a reproductive nature, which do not require interpretation, argumentation, or reference to personal experience, are easily replaced by AI. On the contrary, assignments based on primary data, situational analysis, and oral argumentation of solutions retain their developmental potential and support the development of leadership thinking among students. Based on the empirical evidence, the study suggests four principles to design ‘counter- template’ assignments which are resistant to AI use: learning through action, individual context, the single-case principle, and evaluation through changing conditions. A corresponding assessment model is proposed, focusing on cognitive autonomy and analytical flexibility of learners. The results of the study can be applied to update the instructional practices within the bachelor’s, master’s and continuing education programs in management and entrepreneurship.
How to link insert
Tkhorikov, B. A. (2026). PRACTICAL ASSIGNMENTS IN BUSINESS EDUCATION: ADAPTING TO THE CHALLENGES OF GENERATIVE AI Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", 20 (2), 122. https://doi.org/10.24412/2076-9121-2026-2-122-135
References
1.
1. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
2.
2. Luo, J. (2024). A critical review of GenAI policies in higher education assessment: A call to reconsider the “originality” of students’ work. Assessment & Evaluation in Higher Education, 49(5), 651–664. https://doi.org/10.1080/02602938.2024.2309963
3.
3. Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19, 26. https://doi.org/10.1007/s40979-023-00146-z
4.
4. Williams, A. (2025). Integrating artificial intelligence into higher education assessment. Intersection: A Journal at the Intersection of Assessment and Learning, 6(1), 128–154. https://doi.org/10.61669/001c.131915
5.
5. Evangelista, E. D. L. (2025). Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education. Contemporary Educational Technology, 17(1), ep559. https://doi.org/10.30935/cedtech/15775
6.
6. Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174. https://doi.org/10.3390/educsci15020174
7.
7. Gonsalves, C. (2025). Contextual assessment design in the age of generative AI. Journal of Learning Development in Higher Education, 34. https://doi.org/10.47408/jldhe.vi34.1307
8.
8. Finkel-Gates, A. (2025). ChatGPT in academic assessments: Upholding integrity. Journal of Learning Development in Higher Education, 36. https://doi.org/10.47408/jldhe.vi36.1491
9.
9. Agostini, D., Picasso, F., & Ballardini, H. (2024). Large Language Models for the Assessment of Students’ Authentic Tasks: A Replication Study in Higher Education. CEUR Workshop Proceedings, 3879. https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_36.pdf
10.
10. Klyshbekova, M., & Abbott, P. (2024). ChatGPT and assessment in higher education: A magic wand or a disruptor? Electronic Journal of e-Learning, 22(2), 30–45. https://doi.org/10.34190/ejel.21.5.3114
11.
11. Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296
12.
12. Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19, 17. https://doi.org/10.1007/s40979-023-00140-5
13.
13. Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559–614. https://doi.org/10.1080/03043797.2023.2213169
14.
14. Freeman, J. (2025). Student Generative AI Survey 2025. Higher Education Policy Institute. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2025/
15.
15. Valcea, S., Hamdani, M. R., & Wang, S. (2024). Exploring the impact of ChatGPT on business school education: Prospects, boundaries, and paradoxes. Journal of Management Education. https://doi.org/10.1177/10525629241261313