Beyond AI (9): 3 takeaways for teachers: Embracing change, distrusting sameness

That transfer is possible from one language to another has also been shown in the narrative: I learnt about second-language teaching methodology for Russian and about pragmatics for my first language German. When I started teaching German at a university in England, I transferred the language teaching methodology from Russian to German and the knowledge of pragmatics from a monolingual context to a bilingual (teaching) context. Given the right motivation and some collaboration with and learning from others, the transfer between language contexts and also from one person to another are possible. This is, after all, what teaching and learning are often about. This is an excerpt from an early draft of a book chapter. The book will be about language teacher education and GenAI. I am posting these in smaller (mostly) self-contained posts. The posts are numbered consecutively. After they will have all come out, I will link them with each other.

In education: Embracing change, distrusting sameness

In teaching, it is almost a truism that change is the only constant. In East Germany until 1990, Russian was the obligatory second language taught at schools. Today, only a minority of Gymnasien (high schools) offer Russian. This was a significant change, if not challenge, for me and many Russian teachers like me. Also in the early 1990s, computers were new for me because they had been unavailable and unaffordable before. And today, GenAI chatbots are new to me as to many of us. My learning about computers helped me to expand my portfolio of language teaching methods and to support my colleagues and students. Similarly, my learning about large language models and artificial neural networks is helpful in my discussions with students and for workshops with world languages teachers. Not rejecting the change and being open, curious, and critical about it, helped me learn. Embracing change – not uncritically – is the prerequisite for my growth as a teacher, also in the context of disruptive technologies (Bower & Christensen, 1995).

Both multimedia (multiple media – print, picture, audio, video – in one document) and hypermedia (hyperlinks breaking the two dimensions of a text and leading the reader to another text) were disruptive technologies (Bower & Christensen, 1995) in language education. They brought easy access to resources for listening comprehension, to give one example, but they also resulted in impoverished corrective metalinguistic feedback being given in web quizzes, as another example (Heift & Schulze, 2015). General chatbots today offer great opportunities for individual conversation practice for students, and they are not equipped to provide consistently accurate feedback, because of their exclusive reliance on patterns of tokens in the (mainly first-language) undocumented training data. The generated synthetic text (Bender & Hanna, 2025) they present to learners and teachers alike is plausible and comes across as very confident, in spite of the token sequence just being built on mathematical probability (Schulze & Hubbard, 2026). Both students and teachers can learn how to make the best use of this new powerful technology by being open to it, learning about it, and interacting with it.

Different technologies (e.g., language labs, desktop computers, mobile digital devices) have often also been associated with different language teaching approaches (e.g., grammar-translation, audiolingual, communicative). In the ongoing post-method era (Kumaravadivelu, 2006), these approaches and methods were bracketed through, for example, concept-based language instruction (Lantolf & Poehner, 2014) or task-based language teaching (Willis, 1996, 2004). A fluid and considered change of methods could be implemented in one lesson and with different digital tools. The new GenAI tools should only be associated with the post-method era. This requires systematic and reflective decision-making and planning by the teacher. It is the various components of the activity system of language-teaching-and-learning that help the teacher choose a specific GenAI tool: What is the learning object(ive)? Who exactly are the students (subject)? What are the rules of the school and what does the curriculum say?

Bender, E. M., & Hanna, A. (2025). The AI con: How to fight big tech’s hype and create the future we want. Harper Collins Publishers.

Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies. Catching the wave. Harvard Business Review, 73(1), 43–53.

Heift, T., & Schulze, M. (2015). Tutorial CALL. Language Teaching, 48(4), 471–490.

Kumaravadivelu, B. (2006). Understanding language teaching: From method to postmethod. Routledge.

Lantolf, J. P., & Poehner, M. (2014). Sociocultural Theory and the Pedagogical Imperative in L2 Education. Vygotskian Praxis and the Research/Practice Divide. Routledge.

Schulze, M., & Hubbard, P. (2026). What the LLM? In Opening AI for Language Learning. https://podcasts.apple.com/us/podcast/what-the-llm/id1886782768?i=1000756376738

Willis, J. R. (1996). A framework for task-based learning. Longman.

Willis, J. R. (2004). Perspectives on Task-Based Instruction: Understanding Our Practices, Acknowledging Different Practitioners. In B. L. Leaver & J. R. Willis (Eds.), Task-Based Instruction in Foreign Language Education: Practices and Programs (pp. 3–44). Georgetown University Press.