DOI: 10.11648/j.si.20261404.14 ISSN: 2328-787X

AI-Assisted Educational Innovation Model for Teaching Lexical Argumentation in Russian Literary Texts

Gong Ping
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This article proposes an educational innovation model for artificial intelligence-assisted analysis of lexical argumentation in Russian literary texts for Chinese learners. The study begins with a practical problem in Russian-literature classes: intermediate learners can often translate individual words, but they do not always see how lexical choices create implicit persuasion, authorial evaluation and cultural meaning. The literary material is Evgeny Vodolazkin’s novel Lavr (known in English as Laurus), especially a fragment organized around the opposition between word and silence. Generative AI is treated not as a source of ready-made commentary, but as a supervised tool for discovery, checking and reformulation. The model includes five steps: AI-based preliminary annotation, textual verification, functional classification, intercultural reflection and teacher-guided reformulation. A classroom case compares the outputs of ChatGPT, Yandex Alice AI and DeepSeek. The classroom case suggests that these tools may help learners notice lexical clusters related to speech, silence, spiritual authority and implicit persuasion, while also pointing to risks of overbroad context, paraphrase-based interpretation and cultural flattening. Rather than presenting a large-scale experiment, the article offers a qualitative classroom design that can be adapted to humanities courses where text evidence, tool choice and teacher judgment need to be coordinated. The value of AI lies in organizing a verified human-AI dialogue that helps students move from translation and retelling to evidence-based analytical writing.

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