Neural networks as a foreign language teaching tool

this study examines the potential applications of neural networks as tools for foreign language instruction, driven by the need to adapt educational processes to digital transformation and the widespread adoption of artificial intelligence technologies. The research aims to evaluate the effectiveness of neural networks, particularly the Twee platform, in automating the creation of teaching materials. AI solutions address pressing challenges such as reducing plagiarism through unique task generation and decreasing instructors' workload. A comparative analysis of Twee's functionality versus general-purpose language models (ChatGPT, DeepSeek, Gemini) reveals that its domain-specific interface significantly minimizes prompt engineering requirements and simplifies AI-assisted pedagogy. The practical relevance is evidenced by automatically generated exercises (discussion prompts, gap-fill activities, lexical tests), demonstrating Twee's capacity to align content with CEFR levels (A1-C2) and learners' age groups. However, legal examination uncovers unresolved copyright issues. Despite neural networks' simulated creativity (e.g., "deep thinking" functions), the protectability of AI-generated materials remains contested. Findings suggest neural networks are currently best deployed for routine educational tasks. Future research should investigate AI's long-term impact on language acquisition efficacy and develop legal frameworks to safeguard authorship amid education's digital transformation.

neural networks, artificial intelligence, foreign language teaching, copyright law, digital transformation of education, higher education pedagogy