ИННОВАЦИОННЫЙ МЕНЕДЖМЕНТ В ЛИНГВИСТИЧЕСКИХ ЦЕНТРАХ: ПРИМЕНЕНИЕ AI-ИНСТРУМЕНТОВ ДЛЯ ЭФФЕКТИВНОГО ФОРМИРОВАНИЯ УЧЕБНЫХ ГРУПП
DOI:
https://doi.org/10.5281/zenodo.20588163Keywords:
инновационный менеджмент, искусственный интеллект, AI-инструменты, лингвистические центры, формирование учебных групп, образовательная аналитика, кластеризация, предиктивная аналитика, персонализация обучения, цифровая трансформацияAbstract
В статье рассматриваются возможности применения AI-инструментов в инновационном менеджменте
лингвистических центров. Особое внимание уделено использованию искусственного интеллекта для эффективного
формирования учебных групп с учётом уровня владения языком, возраста, мотивации, образовательных целей,
темпа усвоения материала и коммуникативных особенностей обучающихся. Обосновано, что традиционные
методы ручного распределения студентов не всегда обеспечивают объективность и точность управленческих
решений
References
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one
tutoring. Educational Researcher, 13(6), 4–16. https://doi.org/10.3102/0013189X013006004
Bloom, B. S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1–12.
Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P.
A. Kirschner (Ed.), Three worlds of CSCL: Can we support CSCL? (pp. 61–91). Open Universiteit Nederland.
Dillenbourg, P., Baker, M., Blaye, A., & O‘Malley, C. (1996). The evolution of research on collaborative learning. In
E. Spada & P. Reiman (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp.
–211). Elsevier.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–
https://doi.org/10.1177/0002764213498851
Long, P. D., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review,
(5), 31–40.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology
Enhanced Learning, 4(5/6), 304–317. https://doi.org/10.1504/IJTEL.2012.051816
Behrens, J. T., & DiCerbo, K. E. (2014). Harnessing the currents of the digital ocean. In J. A. Larusson & B. White
(Eds.), Learning analytics: From research to practice (pp. 39–60). Springer. https://doi.org/10.1007/978-1-4614-3305-
_3
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White
(Eds.), Learning analytics: From research to practice (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-
_4
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions
on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/
TSMCC.2010.2053532
Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and
teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial
Intelligence in Education, 24, 470–497. https://doi.org/10.1007/s40593-014-0024-x
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The Knowledge-Learning-Instruction framework: Bridging the
science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798. https://doi.org/10.1111/
j.1551-6709.2012.01245.x
Tomlinson, C. A. (2001). How to differentiate instruction in mixed-ability classrooms (2nd ed.). ASCD.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory
and cooperative learning. Educational Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09339057
Fullan, M. (2016). The new meaning of educational change (5th ed.). Teachers College Press.
Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational
Psychologist, 47(2), 71–85. https://doi.org/10.1080/00461520.2012.667064
Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps. Educational
Research, 61(3), 257–273. https://doi.org/10.1080/00131881.2019.1625716
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT

This work is licensed under a Creative Commons Attribution 4.0 International License.