Panta Rhei Enterprise

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. Commonly available machine translation tools are now similar to GenAI-based chatbots, because they are also based on large language models. The generation of a text in another language or the translation of a text from language A to language B with a GenAI tool is literally just a prompt and/or a click away. Of course, fast translation tools can be very helpful in many situations. Yet, the habitual and exclusive use of machine translation has the potential to reduce communication to the exchange of forms, as it certainly will not lead to a negotiation of meaning. The simple lookup of an answer to a second-language teaching prompt or the generation of a text to produce a learning task result are at least a missed practice opportunity and are much less likely to lead to learning.

Language learning is a long and, for many, difficult process. With the advent of GenAI, teachers need to be able to explain – even more so than before – why it is worthwhile to learn a language, while tools enable computer users to mimic the command of another language. In language learning, students can have the experience that language is more than an intricate assembly of linguistic forms that follow some system of accuracy rules. If students are able to look underneath and beyond, and understand, these language forms – this competence Kramsch (2006) calls symbolic competence – then they have obtained entry to another speech community. Computational tools, on the other hand, can only provide access to sets of linguistic forms or character strings, also in other languages (see the Chinese Room Argument (Searle, 1980)). Teachers need to be able to motivate their students such that they choose the longer yet more fruitful path of language practice and learning rather than that of the rapid generation of plausible texts.

This blog post is an excerpt from the manuscript for Schulze, Mathias (2025). The impact of artificial intelligence (AI) on CALL pedagogies. In Lee McCallum & Dara Tafazoli (eds) The Palgrave Encyclopedia of Computer-Assisted Language Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-51447-0_7-1. 
In 2024, I wrote this encyclopedia entry as my first attempt of gaining a better understanding of what was going on after GenAI burst into Language Education.

References

Kramsch, C. (2006). From communicative competence to symbolic competence. Modern Language Journal, 90(ii), 249–252.

Searle, J. (1980). Minds, brains, and programs. Behaviorial and Brain Sciences, 3(3), 417–457.

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. The people behind the scenes are:

Chris Brown: producer, editor
Shahnaz Ahmadeian Fard: moderator
Mari Ocando Finol: production coordinator

We have plans to push out the recorded conversations as podcasts, but that takes time. So, here they are for your listening pleasure:

2026

#	title	recorded
003	GenAI stuff you need to know now (part 2) Phil Hubbard & Mat Schulze	2026-01-21
002	GenAI stuff you need to know now (part 1) Phil Hubbard & Mat Schulze	2026-01-21
001	What the LLM? Mat Schulze & Phil Hubbard	2025-12-11

OAILL is supported by the Language and Applied Research Center at San Diego State University (SDSU-LARC) and the Southern Area International Languages Network (SAILN).

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.

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. For each of these, teachers and students can and should explore their effective use together paying attention to emerging AI literacy (Bowen & Watson, 2024, p. 42ff.) and to considerations of equity and transparency.

Dialog partner: Starting with a prompt that defines the role and context the GenAI tool is asked to assume or simply with a general question, which will be refined and modified at each turn, students can enter engage in an individual chat in the target language. They can ask for their mistakes to be pointed out and for help with formulations. Tools that have the pedagogic prompts ready for a successful interaction with language learners are increasingly becoming available. In general, teachers should guide these interactions of individual students with the computer, by being available to intervene with help and correction, when necessary.
Natural-language search interface: Some GenAI tools rely on both a large language model and a powerful search algorithm and index. This allows students to search the internet, using conversational utterances. Searches can be done in the target language and using an iterative approach, modifying the search and correcting the search engine, if the results were not what the students were looking for.
Sketch tool: Chatbots based on GenAI can be used to generate drafts of passages, lists of ideas, topics, or components, and generally draft documents for brainstorming.
Text adapter: Texts can be summarized, shortened, or extended. They can be adapted for a different audience or a different proficiency level. Vocabulary lists or lists of specific grammatical construction can be extracted for focused learning.

This blog post is an excerpt from the manuscript for Schulze, Mathias (2025). The impact of artificial intelligence (AI) on CALL pedagogies. In Lee McCallum & Dara Tafazoli (eds) The Palgrave Encyclopedia of Computer-Assisted Language Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-51447-0_7-1. 
In 2024, I wrote this encyclopedia entry as my first attempt of gaining a better understanding of what was going on after GenAI burst into Language Education.

Teachers should set a good example by using GenAI appropriately, also in preparation for their classes and the tests they give to students. This transparency will be the basis for a pedagogically effective and ethical use of these powerful tools by students. The collaborative exploring is necessary to give students the opportunity for reflection and the chance to use GenAI as tools for their language learning and not instead of their language learning.

References

Bowen, J. A., & Watson, C. E. (2024). Teaching with AI. A practical guide to a new era of human learning. John Hopkins University Press.