Teachers and students alike have used or can use GenAI-based chatbots in their first language. They produce texts that at least sound plausible in the commonly taught languages and in some of the less commonly taught languages, if large language models are available. For these languages, GenAI tools can also function as a dialog partner, natural-language search interface, sketch tool, and text adapter to augment CALL pedagogy.

From late 2022 to early 2025, several LLM-based chatbots were released, such as ChatGPT, Claude, Gemini, Qwen, Llama, and DeepSeek. All of them can generate conversational responses with remarkable linguistic accuracy and contextual appropriateness and at almost turn-taking speed. Such chatbots can be prompted to adjust the proficiency level of the output to the learner (but see Uchida (2025) for the challenges with adjusting for CEFR levels) and can stick to the desired topic and only use appropriate vocabulary.

Before 1916, the city of Kitchener, Ontario, Canada, was called Berlin. Ten years ago in 2016, colleagues from the Waterloo Centre for German Studies and I organized a panel discussion that marked the 100th anniversary of that name change. This discussion took place and was recorded in the Kitchener Public Library. Carl Zehrs, the former mayor of Kitchener, was our moderator.

The degree to which generative artificial intelligence (GenAI) has rapidly infiltrated education is unparalleled. Language education has been particularly impacted because GenAI tools process and generate the learning objective of that education, i.e., human language. Language teacher education programs have been faced with addressing GenAI since the public release of ChatGPT in November 2022, and we anticipate that many recent and future graduates will have had some formal education that includes it.

Twitch account for The Distributed AI Research Institute (DAIR).

We propose 10 areas for language teachers to develop fundamental knowledge and skills in. While these do not constitute an exhaustive list, they cover a wide range of the main applications of GenAI for language education. We suggest that teachers devote a few hours to exploring each as soon as possible, reflecting critically on how they may be incorporated in current teaching contexts.

We begin by reviewing four frameworks covering teacher competencies for AI and GenAI in education as a whole – UNESCO (2024), Educause (2024), ISTE (2024), and Paradox Learning (2023, 2025). This is part of a draft of an article I wrote with Phil Hubbard. He was the main writer of this part. In this paper, we are proposing a way in which teachers can organize their own professional development (PD) in the context of the rapid expansion of Generative AI…

We share a responsibility to create and use empowering metaphors rather than misleading language, write Emily M. Bender and Nanna Inie.

This post is a replica of the original home page. (The current home page of the site is simply set to the list of recent posts in reverse chronological order.) Just thought you might be interested what is behind this blog. If you are a regular reader, ... it cannot always be about AI 😉 Panta Rhei — everything flows…

Seven Lessons There has always been some interaction between AI and language and learning for the last 70 years. In computer-assisted language learning (CALL), people have worked on applying AI – a…

7. Gradual release of responsibility Instructional sequences and other learning processes are structured according to pedagogical guidelines and principles and specific teaching methods. For reason…

Of course, a language teacher is more than a benevolent conversation partner. In AI, an intelligent tutoring system (ITS) would be more akin to a language teacher than a chatbot would. An ITS consists of three interacting components (see Heift & Schulze, 2007): The expert model, which captures the domain knowledge or the information that students should learn; The tutor model, which makes decisions about the instructional sequences and steps as well as appropriate feedback and guidance for the group as a whole and for individual students;

In the interaction with a chatbot, one can change the language or prompt the machine to reply in another language than that of the prompt or request a translation of a text generated previously. It is therefore not surprising that dedicated machine translation (MT), such as Google Translate and DeepL, relies on LLMs and thus artificial neural networks in the way that GenAI chatbots do.

6. Dynamic individualization Even though a GenAI is not an ITS, as some ICALL systems were, can it consider and appropriately respond to individual learner differences (Dörnyei, 2006)? On the one h…

This is part of a draft of an article I wrote with Phil Hubbard. He was the main writer of this part. In this paper, we are proposing a way in which teachers can organize their own professional development (PD) in the context of the rapid expansion Generative AI. We call this PD sustained integrated PD (GenAI-SIPD). Sustained because it is continuous and respectful of the other responsibilities and commitments teachers have; integrated because the PD activities are an integral part of what teachers do anyway; the teacher retains control of the PD process.

5. Recording learner behavior and student modeling The intelligent tutoring systems in ICALL had this knowledge stored in a student model (Schulze, 2012). Student modeling (e.g., Bull, 1993; Bull, …

Over the past decades, those of us interested in computer-assisted language learning have repeatedly seen new technologies arrive with promises to transform language education. From early interactive grammar exercises to multimedia CD-ROMs, from learning management systems to mobile apps, each sparked both excitement and trepidation. Generative artificial intelligence (GenAI), however, is different. The sudden arrival of large language models and their chatbots into everyday life in late 2022 did not just add another teaching and learning tool but set in motion a fundamental change of the environment in which teachers teach and learners learn.

4. Appropriate error correction and contingent feedback Rather than focusing on engaging the learner in communicative interaction, learning with ICALL systems was often based on the assumption that…

3. Varied interaction in language-learning tasks The human-machine conversation often works because we are used to adhere – even if the machine cannot and is not — to Grice’s four maxims of c…

2. Communication in context Oxford (1993) desired that “communicative competence must be the cornerstone of ICALL”  (p. 174), noting that many ICALL projects of her time did not me…

1. Exposure to rich, authentic language The texts – or the language – that a computer can understand or generate depend on its capacity for NLP. Computer scientists added the adjective 'natural' because the parsing of programming language(s) was possible, and necessary, before they turned to parsing texts produced by humans. In early NLP, computational linguists wrote grammatical rules and compatible dictionaries in programming languages such as Prolog and LISP.

What is the difference between humans and AI? You are wondering ... So was I ... In July of this year, I gave a keynote presentation at JALTCALL under the title "Language Learning with GenAI: Bridging the gap or burning the bridge." JALT is the Japanese Language Teacher Association, and JALTCALL is its large special interest group – I believe they have between 200 and 300 members in the interest group – in computer-assisted language learning.

A prediction about the future of language teaching when GenAI is rapidly evolving? That's tough. Predictions are difficult, especially about the future. Usually Niels Bohr is credited with this bonmot. Apparently, it was the Danish politician Karl Kristian Steincke who said it first: Det er vanskeligt at spå, især når det gælder fremtiden. We are more trying to explore how language teachers can begin to shape the future of language teaching with GenAI.

Photo by Victor Freitas on Pexels.com It feels like the chatter about AI (as they say, when most people mean generative AI only) seems to be as vast as the ocean ... And that is a good thing; we are dealing with a complex rapidly moving challenge-cum-opportunity. Recently, I wrote about 7 lessons from 70 years in the context of AI…