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.
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