The English language teacher in the era of generative AI: new roles and instructional interaction models

Abstract

The article explores the shifting role of the English language educator in higher education, driven by the rapid integration of generative AI into pedagogical practices. The studyʼs relevance stems from the urgent necessity to adapt traditional pedagogical models to an innovative digital environment and overcome conservative approaches in linguodidactics. The author substantiates the transition from a conventional binary “teacher – student” scheme to an innovative triadic “student – AI assistant – teacher” model, highlighting its immense potential for training future philologists and translators in the 21^st century. It is demonstrated that the teacherʼs function is fundamentally evolving from a knowledge transmitter to a learning experience designer, a mentor, and a moderator of complex cognitive interaction. Particular focus is placed on the “AI as opponent” strategy, implemented through “linguistic duels” and structured counter-argumentation mechanisms to enhance critical thinking, linguistic intuition, and professional reflection among students. The study employs pedagogical modeling of discursive strategies for interacting with GenAI tools (Gemini, NotebookLM) alongside the case study method. Specifically, the article presents an original framework of educational cases aimed at developing studentsʼ ability to justify and defend their philological interpretations through multi-level prompt engineering. This involves mastering techniques such as chain-of-thought, role-based, context layering, style-shift, and modeling real-world translation challenges in Deep Research mode. The author argues that utilizing AI as an intellectual simulator transforms linear text generation into a sophisticated non-linear process of expert modeling and semantic verification. The conclusions emphasize that the triadic model fosters shared agency between humans and algorithms, where the student and teacher remain the final experts. These principles of technological transparency and cognitive conflict help maintain the intellectual independence of future specialists in the AI era.