language learning – Panta Rhei Enterprise

Education and AI: Tool versus tutor

Of course, a language teacher is more than a benevolent conversation partner. In AI, an intelligent tutoring system (ITS) would be more akin to a language teacher than a chatbot would. An ITS consists of three interacting components (see Heift & Schulze, 2007):

The expert model, which captures the domain knowledge or the information that students should learn;
The tutor model, which makes decisions about the instructional sequences and steps as well as appropriate feedback and guidance for the group as a whole and for individual students;
The student model, which records and structures information about the learning progress and instruction received, domain beliefs and acquired information, as well as the learning preferences and styles of each student.

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

Only if the sole learning objective is conversational ability, can one assume that the LLM has elements of an expert model. The other two models, however, cannot be mimicked by a GenAI tool. Consequently, teachers still have to teach – determine instructional sequences, time appropriate feedback, remember and work with an individual student’s strengths and weaknesses – also when using GenAI tools in various phases of the learning process. GenAI tools can provide multiple ideas for engaging learning activities, texts for reading with a ready-made glossary, or drafts of an entire unit or lesson plan. However, it is the teacher who must understand, select, adapt, and implement them. The entire teaching process and its success are still the responsibility of the teacher.

Grammar teaching in antiquity — Grammar teaching in Ancient Rome (generated by ChatGPT 5.1)

In an educational institution, teachers can meet this responsibility because learners normally trust their expert knowledge, because teachers have been trained, certified, and frequently evaluated. The same is not (yet) true of GenAI tools. They have been trained through machine learning, but their semantic accuracy and pragmatic appropriateness have often been found lacking. The generated text is plausible, but not necessarily factually correct or complete. This way, GenAI output is an insufficient basis for successful learning. This becomes apparent not only when one tries out a GenAI tool in the area of one’s own expertise, but also when one looks back on what teachers have said about the various levels of trustworthiness of internet texts, which also formed the basis for the machine learning for LLMs, for the last thirty years: sources have to be checked and validated. In machine learning for LLMs, the texts and sources are not checked nor validated. This can impact the content accuracy of LLM output. Of course, learners cannot be expected to check the accuracy of information they are only about to learn; believing the truth value of the information is a prerequisite for learning. Critical analysis and questioning the information learnt is always a second step. Also, first studies have emerged that show that GenAI can create the illusion of knowing and thus of learning (Mollick, 2024); consequently, chatbots are not always a tool for successful learning.

The main thing to remember is: these GenAI chatbots are a tool and not a tutor – more like a hammer than an artisan, more like a dictionary than an interpreter, and more like an answering machine (remember those?) than a teacher.

References

Heift, Trude and Mathias Schulze (2007). Errors and intelligence in CALL: Parsers and pedagogues. Routledge.

Mollick, E. (2024). Post-apocalyptic education: What comes after the homework apocalypse. https://www.oneusefulthing.org/p/post-apocalyptic-education

Translation and AI: Separated by a common language

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.

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

Generative AI and the Future of Language Classrooms

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.

Language classroom in 2033, as imagined by ChatGPT

ChatGPT 5 imagined this language classroom in 2033

Public discourses have reflected this sense of rupture. Media headlines made utopian promises – AI as patient tutor, instantaneous translator, or personal coach – or raised dystopian warnings of cheating, job loss, or cultural and cognitive decline. University administrators and school boards scramble to update policies, while journalists speculate about the “death of the essay” or the “end of second-language learning.” These narratives are dramatic, but they miss an important point. They miss what matters to many teachers and students: the daily reality in which they are working with and, at times, against a very complex and powerful technology in their classrooms and in their lives. Teachers must make timely and important decisions: whether and how to allow or ban GenAI use in learning activities and especially in graded assignments; how to talk to students about plagiarism and authorship; how to redesign assessment; and how to build critical AI literacy. In staff rooms, professional development workshops, and teacher networks, conversations are often pragmatic: Which prompts work best for this language-learning activity? How do we prevent overreliance? How do we foreground human interaction, communication, and thinking?

This is a draft of my foreword for a book that has now come out:

Louise Ohashi, Mary Hillis, & Robert Dykes (Eds.) Artificial intelligence in our language learning classrooms. Candlin & Mynard ePublishing.

https://www.candlinandmynard.com/genai1.html

This book tackles these questions. It does not treat generative AI as an external force to be admired or feared from a distance. Instead, it examines how GenAI is already being used in classrooms and to the benefit of and in collaboration with the students. Its chapters speak to classroom practice, to pedagogy, and to the professional and ethical responsibilities of teachers. From my perspective as someone who has long been interested in the intersection of technology, language, and education, this is precisely what we language teachers need. What matters is whether teachers can integrate these tools without losing sight of the social, cultural, and emotional dimensions of language and of learning.

By grounding the discussion in theory, research, and classroom experience, the book provides what teachers most need: different perspectives, clear guidance, and thoughtful reflection. Within this broad focus, all book chapters foreground teacher agency. Public discourses sometimes frame educators as passive, either as victims of a disruptive technology or as gatekeepers tasked with policing it. In this book, teachers are shown as active participants: experimenting with GenAI in their classrooms, guiding learners in prompt design, encouraging reflection, embedding AI literacy into their pedagogy, … This emphasis is crucial. If GenAI is to have a useful place in language education, it must be under teacher control and be shaped by pedagogical priorities that, in turn, are rooted in both educational principles and technological awareness.

Reading across the chapters, one finds a sense of the broader ecology in which language education now takes place. Generative AI is not an add-on; it reshapes the communicative environment itself. Learners increasingly write, read, and converse in contexts where GenAI is ubiquitous. Teachers, therefore, cannot simply teach “around” AI; they must teach “with” and “about” it. That means equipping students not only to use AI tools effectively, but also to critique them and to understand their both their capabilities and limitations.

As I read this book’s contributions, I was reminded of an important lesson: technology can disrupt pedagogy and education, but it does not determine how and what we teach. It is always teachers, working with learners in real contexts, who determine whether a tool becomes a crutch, a distraction, or a catalyst for learning. This book exemplifies that spirit. It offers new ideas, outlines paths for further inquiry, and sharpens the (empirical and theoretical) lens for teacher reflection. It shows how generative AI can be questioned, adapted, and contextualized rather than either blindly adopted or hastily rejected.

When you read this book, my hope is that you will also come away not only with new ideas for classroom practice, but with renewed confidence in the changing role of teaching and teacher. Generative AI may be unprecedented in its large scope and powerful capabilities, but the fundamental task remains the same: to create environments where students learn to use language meaningfully and comfortably and develop their empathy for other people and peoples, for their customs and cultures. The chapters that follow offer help with this task.