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.
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 way, these chatbots are far superior to those of early applications of AI and especially NLP in CALL (Schulze, 2025).
German chatbot. Generated by ChatGPT 5 as an illustration
Is a conversation with a chatbot the same as a human conversation? LLM-based chatbots have been compared to “stochastic parrots” (Bender et al., 2021), parrots that produce plausible utterances by chance. The computational linguists Bender et al. (2021) stated that
coherence is in fact in the eye of the beholder. Our human understanding of coherence derives from our ability to recognize interlocutors’ beliefs … and intentions … within context … That is, human language use takes place between individuals who share common ground and are mutually aware of that sharing (and its extent), who have communicative intents which they use language to convey, and who model each others’ mental states as they communicate (p. 616).
For language learners, this means that it is on them to control and steer the conversation with the machine. What they say – their prompt – determines what the machine says much more so than with a human interlocutor, who will reason about the student’s intention, which is ‘underneath’ the utterance. The machine will calculate the probability of subsequent word forms. The problem is compounded by the chatbot’s attempt to keep the conversation engaging with immediate questions, which makes it more difficult for the learner to steer the conversation. Similar challenges are to be considered in the other direction: the learner’s understanding of the generated text. Chatbots generate plausible texts that correspond to the student’s input because of highly sophisticated pattern matching processes; chatbots do not generate meaning. We, however, are used to decoding meaning from text. So, it is the learner imbuing the text with meaning when reading the machine output. Students are using their world knowledge, their linguistic capital, and their contextual awareness for interpreting the machine’s output. This is normally based on the human assumption that the chatbot “understood” the prompt. However, machines do not understand text in the way humans do – Hariri (2024) prefers the term alien intelligence for AI, because of their way of processing being radically different – they conduct a fast, sophisticated mathematical analysis and then generate a matching piece of text based on their pre-training. This interaction is akin to that with a calculator, which also cannot understand mathematics and can calculate accurately at great speed. Calculators are faster than humans and are more consistent without making mistakes, and so are LLM-based chatbots in both understanding and generating. So, chatbots have the advantage of speed, accuracy, consistency, and task focus – all without fatigue or stress. Yet, they are lacking in emotional intelligence, awareness of the situational context, and personal memory, for example, of similar conversations with the same person in the past.
Individual conversational practice with a GenAI chatbot is feasible and practical within the context of second-language development. Students are exposed to rich authentic language in the process (Schulze, 2025). However, a genuine negotiation of meaning does not take place, because both text understanding and meaning generation are only done by the learner. Clarification requests, confirmation checks, comprehension checks, paraphrasing, and repair – all part of a negotiation of meaning – can only come from the learner and, unless prompted specifically, do not come from the machine. Feedback on communicatively successful utterances, as we find it in the negotiation of meaning in human conversations, is as yet a challenge for GenAI. Its strengths are in being able to process learner input that contains one or more errors. Such errors often get corrected seamlessly in GenAI’s response or in the prompted correction.
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
In many languages – the ones that are well represented on the internet such that they are a good basis for deep learning – GenAI chatbots can be “patient” and “focused” conversation partners for language learners. They can also go beyond simple turns in a written or spoken conversation and generate various text types for learners and teachers alike. Reading texts, quizzes, instructional sequences, lesson objectives, essays, letters, emails, and others can all be generated. This can create the illusion that a chatbot can be a language tutor.
References
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Harari, Y. N. (2024). Nexus: A brief history of information networks from the Stone Age to AI. Random House.
Schulze, M. (2025). 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
Uchida, S. (2025). Generative AI and CEFR levels: Evaluating the accuracy of text generation with ChatGPT-4o through textual features. Vocabulary Learning and Instruction, 14(1), 2078. https://doi.org/10.29140/vli.v14n1.2078
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. Moorhouse & Kohnke (2024) provide initial insights from a group of language teacher educators on this topic. But what about those who have already completed their formal education and are in the language teaching workforce, the millions of individuals across the world actively teaching languages at all levels?
This is part of a draft of an article I wrote with Phil Hubbard. He wrote 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. 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
UNESCO (2024) has recognized the immediate need for AI competency across the board in education and why it should be addressed.
Al can pose significant risks to students, the teaching community, education systems and society at large…In education, Al can reduce teaching and learning processes to calculations and automated tasks in ways that devalue the role and influence of teachers and weaken their relationships with learners. It can narrow education to only that which Al can process, model and deliver. Finally, it can also exacerbate the worldwide shortage of qualified teachers through disproportionate spending on technology at the expense of investment in human capacity development (p. 13).
So, given this litany of dangers, what do we think language teachers need to know and be able to do to achieve a functional level of expertise so that they can safely leverage the affordances of GenAI to improve rather than degrade language learning processes and outcomes? How can language teachers and language programs support them in accomplishing this goal?
In our position paper, we address these questions by focusing on the importance of understanding the fundamentals of AI and its subset GenAI, as recognized in several AI competency and literacy frameworks. For example, the UNESCO (2024) AI Competency Framework for Teachers states that at the lowest of their three levels of AI competency, “Teachers are expected to acquire basic conceptual knowledge on AI, including: the definition of AI, basic knowledge of how AI models are trained, and associated knowledge on data and algorithms” (p. 30). The Educause (2024) Durable AI Literacy Framework, targeted at tertiary institutions, goes further: “Faculty must grasp the core principles of AI, including machine learning, natural language processing, and neural networks. This foundational knowledge is crucial for understanding how AI operates and what its potential applications are in various academic disciplines.” Other frameworks we discuss, such as those from the International Society for Technology in Education (ISTE) and Paradox Learning, echo this need for teacher understanding.
Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290
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