Mat Schulze

Timothy Snyder on the Holocaust as History and Warning When I served as the director of the Waterloo Centre for German Studies, I started the Jacob and Wilhelm Grimm Lecture, the annual flagship event of the Center. For the last lecture that I organized - with a lot of help from many colleagues - we were fortunate to be able to host the historian Timothy Snyder in early 2017.

A recording of the Grimm Lecture 2017

Decorative

🚨 CALICO is now accepting applications for our annual Outstanding Graduate Student award!

Learn more and find the application form here 👉 bit.ly/CALICOGradAw...

#GradStudent #CALL #ComputerAssistedLanguageLearning

Learner motivation and GenAI
Language teachers know that learners need to obtain and then to maintain a level of motivation in their second-language learning and language use. Motivation has become more important in the context of GenAI, as the example of recent advances in machine translation show.

Opening AI to Language Learning (OAILL) ... is a series of recorded conversations on topics at the intersection of Artificial Intelligence (AI), in particular Generative AI (GenAI), and Language Education. We have started using the acronym OAILL, pronounced /owaıl/. With my accent this sounds like the German weil (=because), but it should be more the w in the English well. The people in conversation are listed in the table below.

We are announcing the (very) soft launch of our series of conversations on Opening AI for Language Learning by posting the first version of the static page on OAILL.

EUROCALL 2026 Conference: Promoting linguistic diversity through CALL

Belfast, Northern Ireland, 8-11 September

Call for Papers, available at the link below.

blogs.ulster.ac.uk/eurocallconf...

Deadline for submissions 2nd March

Learn more about EuroCALL at: eurocall-languages.org

Second-language use in dialog … with a GenAI-based chatbot 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.

Recently, I revisited the first paper I wrote (2024) on things GenAI. Here is a little piece of it on the things that can be done in a dialog of a learner or a teacher with the machine.

Conversation and AI: Language in the wild? 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.

This blog post is about something the LLM-based tools are very good at: conversation.

Von Berlin to Kitchener – changing the name of Canadian city 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.

Once in a while, I take a little break from things GenAI. I dug up a couple of older video recordings that are on the internet. In this one we are talking about: what's in a name? of a city? and 100 years later?

Professional development and GenAI 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.

What can the professional development for in-service language teachers look like in the times of GenAI?

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

Mystery AI Hype Theater 3000 is back for our first live stream of 2026, wherein @alexhanna.bsky.social and I will look into how our health care system is suffering from AI-hype-itis:

Monday, Jan 26, noon PT,
twitch.tv/dair_institute

Ten skill and knowledge areas for (language) teaching with GenAI 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.

The sustained integrated professional development for GenAI (GenAI-SIPD) has 10 knowledge and skill areas and 7 principles. Here are the 10 areas for teachers to look at. In short sips, we call it SIP(p)Ded.

What teachers need to know now about GenAI 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…

Phil Hubbard is looking at four different frameworks for (language) teachers working with GenAI.

We Need to Talk About How We Talk About 'AI' | TechPolicy.Press We share a responsibility to create and use empowering metaphors rather than misleading language, write Emily M. Bender and Nanna Inie.

Anthropomorphizing language conceals the limitations of AI, promoting misplaced trust. @emilymbender.bsky.social & @nannainie.bsky.social suggest focusing on a system’s functionalities: instead of saying a model is “good at” something, say what it is “good for." www.techpolicy.press/we-need-to-t...

Navigating change — the Panta Rhei enterprise 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…

The Panta Rhei blog also has a couple of static pages. This post is a replica of one of them. The main one ...

Language Learning and AI: 7 lessons from 70 years (conclusion) 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…

Language Learning and AI: 7 lessons from 70 years (conclusion) pantarhei.press/2025/11/27/a... This one concludes the little series of posts, by bringing together all 7 lessons from what we knew about AI and language learning before ChatGPT came on the scence in late 2022.

Language Learning and AI: 7 lessons from 70 years (#7) 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…

Language Learning and AI: 7 lessons from 70 years (#7) pantarhei.press/2025/11/21/a... This last of the 7 lessons focuses on pedagogy.

Education and AI: Tool versus tutor 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;

Why an LLM-based chatbot is not a language teacher ...

Translation and AI: Separated by a common language 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.

What does machine translation in 2026 have in common with chatbots? The same underlying technology – artificial neural networks. What does that have to do with language teaching?

Language Learning and AI: 7 lessons from 70 years (#6) 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…

Language Learning and AI: 7 lessons from 70 years (#6) pantarhei.press/2025/11/16/a...

And how about individualization ... can chatbots do that for language learners?

Seven principles of sustained integrated professional development (GenAI-SIPD) 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.

7 principles of sustained integrated professional development for language teachers in times of GenAI

Language Learning and AI: 7 lessons from 70 years (#5) 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, …

Language Learning and AI: 7 lessons from 70 years (#5) pantarhei.press/2025/11/10/a...

... and here is a technical reason why a chatbot is a good conversation partner for a learner and worse at teaching than the guy down the street, who sells ice cream and who is an excellent conversationalist

Generative AI and the Future of Language Classrooms 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.

A recent book on Artificial Intelligence in Our Langauge Learning Classrooms.

Language Learning and AI: 7 lessons from 70 years (#4) 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…

Language Learning and AI: 7 lessons from 70 years (#4) pantarhei.press/2025/11/05/a...
My personal favorite of the 7 lessons from what we learnt in the time before 2022. Why teachers and learners should be cautious when some claims a chatbot can give you metalinguistic corrective feedback.

Language Learning and AI: 7 lessons from 70 years (#3) 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…

Language Learning and AI: 7 lessons from 70 years (#3) pantarhei.press/2025/10/30/a... Looking at what teachers have done with AI before ChatGPT-3 in late 2022 and at what can be and should be or should not be done with GenAI chatbots.

Language Learning and AI: 7 lessons from 70 years (#2) 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…

Language Learning and AI: 7 lessons from 70 years (#2) pantarhei.press/2025/10/26/a...

Learning some lessons from the past takes a little writing. This is about lesson 2

Language Learning and AI: 7 lessons from 70 years (#1) 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.

Lesson #1: updated version

Humans vs. AI: The real difference 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.

Humans vs. AI: what is the difference. And also, who or what is better? As they used to say: Read all about it, ...

**CALL FOR BOOK PROPOSALS** The book proposal deadline for the 2028 Advances in CALL Research and Practice Book Series is #September30 🔗 See the CfP here: shorturl.at/Tby36

Volumes address a variety of topics that reflect the breadth of diversity in the field of #ComputerAssistedLanguageLearning

GenAI and the future of language teaching 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.

What is the future of language teaching? That's not what I am worrying about here. I am thinking about what language teachers can do now t shape the future of language teaching with GenAI.

If you would like to know what people did with AI (in language learning) before ChatGPT burst on the scene in late 2022, and what this tells us about the current developments in generative AI, ...