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
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

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.

Book cover: Artificial Intelligence in Our Langauge Learning Classrooms By Ohashi, Hillis, and Dykes (eds.)

Language Learning and AI: 7 lessons from 70 years (conclusion)

Seven Lessons

There has always been some interaction between AI and language and learning for the last 70 years. In computer-assisted language learning (CALL), people have worked on applying AI – and they called it ICALL – for almost 50 years. For GenAI, what can we learn from these efforts of working with good old-fashioned AI for such a long time?

Photo by Julia M Cameron on Pexels.com
My inspiration for this title came from the book  
Snyder, T. (2017). On tyranny: Twenty lessons from the twentieth century. Tim Duggan Books.

I am sharing these early drafts of a book chapter I published in
Yijen Wang, Antonie Alm, & Gilbert Dizon (Eds.) (2025), 
Insights into AI and language teaching and learning.
Castledown Publishers.

https://doi.org/10.29140/9781763711600-02.

In conclusion, we will recapitulate and condense the seven lessons that we can learn from ‘good old-fashioned AI’ and ICALL with its declarative knowledge, engineered algorithms, and symbolic NLP and see how they can be applied to GenAI with its machine-learnt complex artificial neural networks.

  1. Exposure to rich, authentic language
    GenAI is capable of providing ample exposure to rich language just in time, on the right topic, and at the right level. Generated texts consist of mostly accurate language forms and are plausible, so that they lend themselves to an interpretation in context by the students. This gives such a text an authentic feel. Here GenAI compares very well to the limited linguistic scope of ICALL systems.
  2. Communication in context
    GenAI, also because of the comprehensive coverage of the LLMs, can sustain conversations with learners on different topics. Its natural language understanding is such that it can take into consideration prior textual context, making any conversation more natural. This was impossible with ICALL systems and chatbots of the past. However, teachers and students need to be aware that they are communicating with a machine, a stochastic parrot (Bender et al., 2021). This requires informed reflection on a new form of communication and learning, to avoid the anthropomorphizing of machine and its output.
  3. Appropriate error correction and contingent feedback
    This is the area where we can learn most from ICALL and tutorial CALL. Especially with giving metalinguistic feedback, GenAI has too many shortcomings. Researchers need to explore how the automatic error correction, which happens frequently, impacts aspects of language learning such as noticing.
  4. Varied interaction in language learning tasks
    This is the area where we have many new opportunities to explore, although we can take inspiration particularly from projects in ICALL and game-base language learning. GenAI is most suitable as a partner in conversation and learning.
  5. Recording learner behavior and student modeling
    Student modeling has a long tradition – not just in ICALL – in AI and education. GenAI tools by themselves are that – tools and not tutors. They can be embedded in other learning systems, but they cannot be used as virtual tutors, because their information about learners and the learning context are serendipitous at best.
  6. Dynamic individualization
    GenAI provides teachers and students with an individual experience with generated texts of high quality.  The adaptive instruction (Schulze et al., 2025 in press), however, which has been an ambition of ICALL research, has not yet been achieved. Broader research and development in AI, beyond GenAI, is still necessary to achieve dynamic individualization in what can truly be termed ICALL.
  7. Gradual release of responsibility
    Since the instructional sequences, pedagogical approaches, and teaching methods are not present in GenAI, teachers need to design the use of GenAI as one of the tools in the learning process carefully. Teachers must not render the control of curricular and pedagogical decisions about activity design, learning goals, lesson contents, and learning materials to the machine.

GenAI, due to its powerful LLMs, has lifted AI in language education to a new quality. Such a disruptive technology shows great promise, provides many additional opportunities, and poses some challenges for teachers, students, and researchers alike.