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.
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. MT was there at the beginning of AI (see above) and was rooted in symbolic approaches, using grammatical rules and lexical items. MT output had to be post-edited by human translators as a matter of course. Such post-editing tasks and the critical reading and analysis of MT output have also been used in language learning with some success (e.g., Niño, 2009). Post-editing was necessary and language learning with MT was useful, because the linguistic accuracy of the MT output was such that it needed post-editing, and it contained more errors than most language learners would make. Today the MT output has high levels of linguistic accuracy and complexity similar to the turns generated by GenAI chatbots. Based on our impression over the years, we would submit that MT output is usually more complex and accurate than the writing of many language learners.
Machine translation in the Cold War. Generated by ChatGPT 5.1
Ohashi (2024) states, “Numerous studies have been conducted on the use of MT in language education” (p.292) recently and discusses several review and literature studies. Lee and Kang (2024) conclude from their study “that MT helped students deliver their meaning, reduce grammatical errors, find appropriate vocabulary, and use expressions and sentence structures beyond their current levels” (p. 12) Yet they also admit that the improved accuracy of the translated texts is not necessarily an indication of successful and sustained language learning.
This is part of a draft of an article I wrote with Phil Hubbard. 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 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.
The full article is available as open access: Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304
Schulze (2025a) highlights some of the problems of generating written texts in the language being learned: the speed of the text generation does not encourage planning, thinking, and intentional engagement and the plausibility of the machine output makes processes of checking and correction very difficult if not impossible, especially for learners who use GenAI tools habitually. This applies in equal measure to GenAI MT. As a matter of fact, the conundrum of GenAI as a powerful tool in multilingual communication becomes clearer when one looks at MT. The speed and linguistic accuracy of translation makes it feasible to have a GenAI-tool-mediated conversation at almost normal interactional speed, with both sides only producing and receiving text in their first language and not being able to check the communicative adequacy and felicity of either. Language teachers will have to determine their stance vis-à-vis MT and, more importantly, the continued motivation to go through the long process of learning another language in times of instant results from machine translation and chatbots.
Summing up this excursion into GenAI, we can conclude with the essence of the seven lessons in Schulze (2025b) and state that
This new technology facilitates the exposure to rich and authentic language and,
GenAI potentially enriches learning with additional opportunities of communicative interaction and language use, because it offers a new way of communicating;
And yet necessary processes of appropriate error correction and feedback as well as documenting learner behavior and dynamic individualization cannot be performed using GenAI tools and remain the responsibility of the teacher.
References
Lee, S. M., & Kang, N. (2024). Effects of machine translation on L2 writing proficiency: The complexity, accuracy, lexical diversity, and fluency. Language Learning & Technology, 28(1), 1–19. https://doi.org/10.1016/j.langlt.2024.73585
Niño, A. (2009). Machine translation in foreign language learning: Language learners’ and tutors’ perceptions of its advantages and disadvantages. ReCALL, 21(2), 241–258. https://doi.org/10.1017/S0958344009000172
Ohashi, L. (2024). AI in language education: The impact of machine translation and ChatGPT. In P. Ilic, I. Casebourne, & R. Wegerif (Eds.), Artificial intelligence in education: The intersection of technology and pedagogy (pp. 289–311). Springer. https://doi.org/10.1007/978-3-031-71232-6_13
Schulze, M. (2025a). The impact of artificial intelligence (AI) on CALL pedagogies. In L. McCallum & D. 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.
Schulze, M. (2025b). ICALL and AI: Seven lessons from seventy years. In Y. Wang, A. Alm, & G. Dizon (Eds.), Insights into AI and language teaching and learning. (pp. 11–31) Castledown Publishers. https://doi.org/10.29140/9781763711600-02
Panta Rhei Enterprise 