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Prompt engineering as a tool for improving the learning process of foreign languages // Pedagogical Review. 2026. Issue 1 (65). P. 46-55

Prompt engineering is increasingly recognized as a pivotal tool for optimizing the foreign language learning process in the context of digital transformation in education. As artificial intelligence (AI) becomes deeply integrated into educational environments, large language models (LLMs) such as GPT, Claude, and Gemini offer unprecedented opportunities for personalized, interactive, and adaptive language instruction. However, the effectiveness of these technologies is not inherent in the models themselves, but critically depends on the user’s ability to formulate clear, structured, and contextually appropriate prompts. This article explores prompt engineering as a metacognitive and pedagogical skill essential for maximizing the educational potential of AI. It analyzes key prompting strategies – zero-shot, oneshot, and few-shot prompting – and demonstrates their differential effectiveness across proficiency levels (A2–C1), with few-shot prompting proving particularly valuable for complex tasks such as grammatical explanations and academic writing. The study identifies five core applications of AI in language education: the generation of authentic texts and dialogues, individualized grammar instruction, creation of tailored exercises, oral practice through AI-driven text and voice interactions, and immediate, detailed feedback on written assignments. Drawing on pilot teaching sessions and original methodological developments, the article presents a comparative analysis of basic versus optimized prompts, revealing a substantial improvement in the relevance, accuracy, and pedagogical value of AI-generated responses when prompt engineering principles are applied. Practical recommendations are proposed for integrating prompt engineering into the curriculum through a four-stage framework: introduction, modeling, practice, and reflection. Furthermore, the article addresses critical ethical considerations, including issues of authorship, academic integrity, technological dependency, and data privacy, advocating for the development of students’ critical digital literacy. It concludes that prompt engineering should be embedded into language curricula as a core component of 21st-century digital competence, empowering learners to become autonomous, reflective, and strategic users of AI in their educational journey..

Keywords: prompt engineering, artificial intelligence technologies, neural network, foreign language teaching, LLM (Large Language Models), digital technologies in education, personalized learning