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…

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 – and they called it ICALL – for almost 50 years. For GenAI, what can we learn from these efforts of working with good old-fashioned AI for such a long time?

With this one, six of the seven lessons have been prepared. Here is a quick list of what I posted in this context before. – historical introduction – Prep #1: exposure to authentic language– Prep #2: communication in context– Prep #3: interaction in language learning with GenAI– Prep #4: appropriate error correction and contingent feedback– Prep #5: …

With this one, six of the seven lessons have been prepared. Here is a quick list of what I posted in this context before. – historical introduction – Prep #1: exposure to authentic language– Prep #2: communication in context– Prep #3: interaction in language learning with GenAI– Prep #4: appropriate error correction and contingent feedback– Prep #5: …

Thus far, I have given a historical introduction and talked about the necessary exposure to authentic language, communication in context, interaction in language learning with GenAI, and appropriate error correction and contingent feedback. All parts are based on a manuscript for a book chapter that I wrote recently. The following describes the basis for lesson #5.

We are onto part 5. All parts are based on a manuscript for a book chapter that I wrote recently. Part 1 gives a historical introduction. Part 2 focuses on the necessary exposure to authentic language and whether this can be done with GenAI. Part 3 looked at communication in context, which is central in language learning. We turned to the role of…

My inspiration for this title came from the book Snyder, T. (2017). On tyranny: Twenty lessons from the twentieth century. Tim Duggan Books. Photo by Mikhail Nilov on Pexels.com What do we know about artificial intelligence (AI) in language teaching and learning already? What can we see if we look back more than two or so years? In the last two years, discourses on generative AI (GenAI) in the academic literature on (language) education, writing, publishing, (machine) translation, computer science, and many other areas as well as in mainstream and specialized media have resulted in a multitude of articles, books, chapters, columns, essays, guidelines, opinion pieces, and tip sheets.

And here is part 4 of a short series. All parts are based on a manuscript for a book chapter that I wrote recently. Part 1 gives a historical introduction. Part 2 focuses on the necessary exposure to authentic language and whether this can be done with GenAI. Part 3 looked at communication in context, which is central in language learning.

This is the third part of a short series. All parts are based on a manuscript that I wrote recently. Part 1 gives a historical introduction. Part 2 focuses on the necessary exposure to authentic language and whether this can be done with GenAI. And I mean exposure and not so-called comprehensible input. 2. Communication in context

Photo by Artem Beliaikin on Pexels.com For the last 30 or so years, I have been working with computers. When working with machines, input is an important concept. In the nineties, I read, heard, and thought a lot about input in the context of language learning and in – what Stephen Krashen called – language acquisition. I struggled with his input hypothesis and his no-interface hypothesis.