Mat Schulze

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.

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

Seven principles of Sustained Integrated Professional Development (GenAI-SIPD)

This is part of a draft of an article I wrote with Phil Hubbard. He was the main writer of 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.

In our context, the term professional development (PD) describes activities engaged in by language teachers after completing their initial formal training (Shawer, 2010). It encompasses a wide range of options. Under the umbrella of continuous professional development, Vadivel et al. (2021) note, “It is a long-term learning process, which is crucial in keeping abreast with the modern changes and developments in the teaching world. There are many ways to develop professionally, either through degrees, courses, workshops, training, or seminars” (p. 2). We do not consider degree programs here, and our target is not professional development across the board (as important as that is) but only in relation to GenAI. We focus on the more targeted PD options, which are often provided by outside experts on-site, online, or during professional conferences. However, Buendía and Macías (2019) observe, “A recurring problem with these one-shot in-service programs is that the knowledge gained is generally disconnected from the teachers’ actual contexts and reality in both practical and conceptual terms” (p. 90).

Photo by Edmond Dantu00e8s on Pexels.com

To counter that problem, a number of PD models and associated principles have been developed. For instance, Darling-Hammond et al. (2017) analyzed 35 empirical studies of successful PD programs, identifying seven common design elements.

They are content focused.
They incorporate active learning strategies.
They engage teachers in collaboration.
They use models and/or modeling.
They provide coaching and expert support.
They include time for feedback and reflection.
They are of sustained duration. (p. 23).

Looking at our GenAI context, for (1), being content focused means that the PD tasks and activities are directly tied to the content and curriculum of the teacher’s language class. The objective is to spend GenAI PD time toward improving the life of the teacher (by measures such as saving time, improving job satisfaction, etc.) and increasing the effectiveness of the language learning experience for the students. For (6), teachers may or may not have access to feedback. This is especially the case if they are relying on non-interactive webinars, prerecorded material, or texts. However, teachers can make time, even a brief time, for reflection. GenAI was not around when most practicing teachers went through their formal training programs. It is thus crucial for teachers trying to understand and appropriately integrate GenAI into their teaching to do so reflectively. Keeping a “GenAI journal” to document this journey is strongly advised. Finally, the notion of sustained duration is crucial for us. The pace at which GenAI has entered our lives is astonishing, and it continues to accelerate. Accordingly, ongoing professional development is becoming a core, continuous element of teaching practice and lifelong professional learning.

Interestingly, design elements (1), (6), and (7) from Darling-Hammond et al. (2017) are also found independently in Matherson et al. (2014). Building on a technological pedagogical content (TPACK) model, they note, “For teachers to overcome shortcomings pertaining to technology use in the classroom, they must be presented with ample professional development opportunities that are embedded in school and classroom practices, sustained over a period of time, and include opportunities for reflection” (p. 48). This view resonates with ours in that it includes the key elements of being sustained, embedded, and reflective.

Before continuing, let us clarify what we mean by sustained and integrated.By integrated we mean it is a part of the regular activities of a language teacher. Where GenAI is concerned, every encounter with it in designing and implementing courses, lessons, tasks, assessments, and so on is a potential PD activity. Determining whether and how to incorporate GenAI is a PD activity. Guiding and monitoring student use is a PD activity. Shifting the GenAI role from tool to collaborator (Pratschke, 2024) is a PD activity. GenAI PD thus becomes ubiquitous in the life of a language teacher, not something solely encountered during workshops, webinars, or conferences. Similarly, by sustained we mean not just occurring in the course of some project, teacher research study, or one-off GenAI experience, but rather daily, or at least quite regularly, throughout an extended time. Ideally, awareness of and critical reflection on GenAI and ultimately other forms of AI for language teaching and learning becomes habitual. There may come a time when GenAI is normalized, fully a part of a teacher’s everyday life and no longer exceptional (Bax, 2003), but that time is not in the immediate future.

Provisional Principles of GenAI-SIPD

Richards (1996) notes that teachers work from principles or “maxims” in their own teaching practice rather than from abstract theoretical frameworks. Thus, we present this guidance in the form of principles because we want it to be clear and actionable so that teachers can use it independently to support their own evolving GenAI autonomy. Teacher autonomy has been a central notion in CALL for some time. For example, Kessler (2010) notes that “the ability to utilize, create, and manage CALL environments for integrated language skill development is a critical foundation upon which CALL teacher autonomy rests” (p. 378), and we believe this is as true as ever in the AI era. Teacher educators, more advanced peers, and institutional authorities can and should share responsibility for supporting teachers in their GenAI journey, but it is ultimately the teachers themselves who need to learn, integrate, and sustain their new skills and knowledge.

Below, we propose seven provisional principles to support teachers’ GenAI-SIPD. We emphasize provisional because while drawn from previous work in computer-assisted language learning and other educational domains, they have not been tested within the GenAI context.

Understand the basics of GenAI to make informed use decisions. Making an informed decision requires having a stock of relevant information to start with and enough of an understanding to know where and how to seek additional information as needed. One objective of this paper has been to provide a foundation regarding AI and GenAI to address this point for language teaching. The details in the first part of the paper and following through on exploring the sample topics and resources will go a long way toward meeting this principle. Importantly, “the basics” are not static. We have witnessed remarkable changes in the past few years and will continue to do so. A corollary of this principle is thus to revisit this foundational understanding regularly and add to it over time.

Experience GenAI as a language learner. There are three parts to this principle. The first involves a teacher taking the role of a true language learner, using one or more GenAI applications to engage in activities to support learning a language you do not already know well. Repeat this over time with a variety of tools to get a solid foundation of learner experience in the GenAI era. The second involves taking any GenAI-mediated assignment or classroom activity you might give and running through it assuming the role of the student to see if the GenAI is doing what you expect it to, making any necessary adjustments. The third is to share your GenAI experiences with students and encourage them to share with one another as well.

Start small and look for uses of GenAI that have immediate value for your current teaching context. As per Puentedura’s (2006) Substitution, Augmentation, Modification, Redefinition (SAMR) model, perhaps begin with substitution. Find existing tasks or activities that look promising in terms of increasing efficiency or effectiveness and try using GenAI as a tool to improve them. Also, be aware that different instances of GenAI integration may take different amounts of time, but that more complex uses may be broken down into smaller elements (Cheng et al., 2015).

Think of GenAI-SIPD as GenAI “sipped.” These “sips” are small amounts of time and energy devoted to GenAI-SIPD. Engaging in these microlearning experiences regularly, making them habitual, and reflecting on them is going to be the most likely way for many to make “sustained” into “sustainable” Kohnke et al., 2024). We understand that practicing teachers do not have the time available to explore new ideas the way teacher candidates in formal courses can, but even a few minutes a day can make a difference. Try to integrate these “sips” into a practical plan for GenAI-SIPD while keeping your own welfare in mind. Document the time spent and lessons learned to engage more deeply and enhance motivation. Here, using GenAI can be compared to daily physical exercise and its positive long-term impact.

Evaluate AI options critically and reflectively. Whatever the language learning goals, it is important to ascertain that a given instance of GenAI use will support them. In particular, it is essential to recognize the difference between GenAI supporting completion of a task vs. supporting learning and language development. First, think through the opportunities and challenges of a particular use of GenAI before using it. Then, if relevant, try it on yourself and monitor what it is like from the learner’s side. Finally review the results critically and decide whether you would use it again and if so, what you might change. This is consistent with Schön’s (1983) teaching cycle of reflecting for action, in action, and on action.

Always consider ethical issues in your and your students’ GenAI use. Ethical considerations are a critical part of using GenAI because of its novelty, power, and opaqueness (Ohashi & Hubbard, 2025). Be aware of issues such as factual and linguistic accuracy, social and cultural bias, privacy and data security, and transparency in when, how, and why GenAI is being used. Be aware of institutional guidelines for acceptable student and staff use or adopt, adapt, or create appropriate ones of your own. These are not entirely new ethical issues for language teachers since many have been around since the Web allowed teachers and students direct free access to resources like digital media and machine translation.

Seek out and nurture partnerships with peers. Although GenAI-SIPD is possible to do on your own, it is easier and more motivating to work and learn with others. The value of a community of practice (CoP) has long been recognized for professional development in general (Lave & Wenger, 1991) and for CALL specifically (Hanson-Smith, 2006). It can be as simple as connecting with one or two other teachers in your program, or it can involve more formal communities such as the AI interest groups of EUROCALL and CALICO. Look for opportunities both to learn from peers and to share your own skills and knowledge with them, locally, in your teacher association, and, if you can, in international organizations. Here GenAI becomes the common ground for fruitful conversations among professionals.

References

Bax, S. (2003). CALL—past, present and future. System, 31(1), 13–28. https://doi.org/10.1016/S0346-251X(02)00071-4

Buendía, X. P., & Macías, D. F. (2019). The professional development of English language teachers in Colombia: A review of the literature. Colombian Applied Linguistics Journal, 21(1), 93–106. https://doi.org/10.14483/22487085.14135

Cheng, J., Teevan, J., Iqbal, S. T., & Bernstein, M. S. (2015). Break it down: A comparison of macro- and microtasks. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 4061–4064. https://doi.org/10.1145/2702123.2702146

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute.

Hanson-Smith, E. (2006). Communities of practice for pre- and in-service teacher education. In P. Hubbard & M. Levy (Eds.), Teacher education in CALL (pp. 301–315). John Benjamins.

Kessler, G. (2010). When they talk about CALL: Discourse in a required CALL class. CALICO Journal, 27(2), 376–392.

Kohnke, L., Foung, D., & Zou, D. (2024). Microlearning: A new normal for flexible teacher professional development in online and blended learning. Education and Information Technologies, 29(4), 4457–4480. https://doi.org/10.1007/s10639-023-12074-6

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Matherson, L. H., Wilson, E. K., & Wright, V. H. (2014). Need TPACK? Embrace sustained professional development. Delta Kappa Gamma Bulletin, 81(1).

Ohashi, L., & Hubbard, P. (2025). Generative AI ethics: Emerging principles for language teachers. In L. Ohashi, M. Hillis, & R. Dykes (Eds.) Artificial intelligence in our language learning classrooms. (pp. 100–125) Candlin & Mynard ePublishing. https://www.candlinandmynard.com/ohashi_hubbard.html

Pratschke, B. M. (2024). Generative AI and education: Digital pedagogies, teaching innovation and learning design. Springer Nature.

Puentedura, R. (2006, August). Transformation, technology, and education. http://hippasus.com/resources/tte/

Richards, J. C. (1996). Teachers’ maxims in language teaching. TESOL Quarterly, 30(2), 281–296.

Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.

Shawer, S. (2010). Classroom‐level teacher professional development and satisfaction: Teachers learn in the context of classroom‐level curriculum development. Professional Development in Education, 36(4), 597–620. https://doi.org/10.1080/19415257.2010.489802

Vadivel, B., Namaziandost, E., & Saeedian, A. (2021). Progress in English language teaching through continuous professional development: Teachers’ self-awareness, perception, and feedback. Frontiers in Education, 6, 757285. https://doi.org/10.3389/feduc.2021.757285

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.