Digital Transformation of Language Pedagogy: Integrating Neural Network Technologies and Artificial Intelligence into the Practice of Teaching Russian in Higher Education
Keywords:
Linguodidactics, Russian as a Foreign Language, generative artificial intelligenceAbstract
Amidst the global digitalization of the educational landscape, the classical paradigm for teaching Russian as a Foreign Language (RFL) requires a fundamental methodological overhaul. This study aims to rigorously quantify the didactic potential of generative artificial intelligence (AI) and adaptive neural network simulators in the process of fostering foreign-language communicative competence among bilingual students. A prospective randomized controlled trial was conducted during the 2024–2025 academic years at Urgench State University, involving students from both philological and non-philological faculties, with a total sample size of 146 undergraduate students (baseline proficiency level: B1). The sample was stratified into a control group (n = 73), which followed a traditional communicative-cognitive methodology, and an experimental cohort (n = 73), whose independent study regimen incorporated conversational AI agents and intelligent training simulators featuring predictive morphosyntactic error correction capabilities. The empirical data obtained confirm that the application of algorithmic scaffolding reduces the level of deep grammatical interference by 38.4% (p < 0.001). Furthermore, a significant increase was observed in the lexical diversity coefficient (Type-Token Ratio, TTR) within the spontaneous speech acts of the experimental group, rising from 0.42 ± 0.05 to 0.68 ± 0.04. The individualized feedback generated by AI in real time effectively optimizes cognitive load and ensures the sustainable transfer of acquired lexico-grammatical patterns into authentic professional communication contexts
References
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
