Hurray, another publication on GenAI in language education.

Here’s my first (Claude-supported) impression:

This eight-section book systematically covers GenAI in language education, starting with foundational knowledge about GenAI tools (with a practical list of applications in the downloadable Appendix 1) , then exploring practical applications for teacher development, lesson planning, assessment, and student support. It addresses ethical considerations, outlines essential teacher skills for GenAI use, and concludes with guidance on opportunities for professional development and staying current with future developments.

Hoping to contribute to raising interest for and stimulate experimentation with AI by practitioners in pre-tertiary education, I am personally particularly interested in AI use that can potentially support the teacher’s role and therefore am enthousiastic to also see a chapter on planning and content creation.
Consequently, I also wholeheartedly share the authors’ view: ‘Lesson planning and preparation can be incredibly time-consuming and challenging for language teachers. Yet, quality planning can lead to more effective language lessons, and is a great place to start using GenAI as it does not require students to engage with the tools directly, and no sensitive data (e.g., students’ work needs to be uploaded to the tools).’

Find a summary of the key elements in the related book chapter below.

Generative AI tools offer remarkable potential for transforming how language teachers approach lesson planning and material preparation. These systems can function as virtual collaborators, generating comprehensive lesson sequences, creating multimodal materials, and suggesting creative activities that teachers might not have considered independently. The appeal is particularly strong given that many language educators feel constrained by commercial textbooks and lack confidence in developing original materials tailored to their specific contexts.

The book demonstrates how AI’s generative capabilities can address long-standing challenges in language education. Teachers can prompt AI systems to create genre-specific materials that align with established pedagogical principles, such as Tomlinson’s framework for effective language learning materials. Teachers can be supported to create materials that provide genuine opportunities for language use and communication.

However, the authors emphasize that AI’s promise must be balanced against fundamental limitations that make co-design essential rather than wholesale delegation to artificial systems. AI lacks the deep contextual knowledge that experienced teachers possess about their specific learners, institutional constraints, cultural considerations, and classroom dynamics. This knowledge gap can result in developmentally inappropriate suggestions, culturally insensitive content, or pedagogically misaligned activities.

The iterative co-design process emerges as the most effective approach, requiring teachers to work collaboratively with AI through detailed prompting and multiple rounds of refinement. Teachers must provide comprehensive information about learners, learning objectives, preferred methodologies, and contextual constraints, then critically evaluate and adapt AI outputs before implementation. This positions teachers as the ultimate decision-makers who bring irreplaceable expertise in understanding their learners’ developmental stages, cultural backgrounds, and evolving needs.

Rather than replacing teacher expertise, this co-design model enhances professional practice by encouraging more explicit pedagogical reasoning and exposing educators to alternative approaches they might not have considered independently. It is likely that the teacher will need to ‘work with’ the AI in an iterative process until the desired plan is created. This means asking follow-up prompts that help the LLM to finetune the response.

My final comment here: the process of defining the information about learners, preferred methodologies etc.. and the critical evaluation of the AI output needed to arrive at results that can function as intended also offer interesting (because dynamic) methodological support to deepen pedagogical content knowledge and development of core competences in teacher education.
Also see ‘A topological exploration of convergence/divergence of human-mediated and algorithmically mediated pedagogy by Keith Turvey, Norbert Pachler

Find the publication here: LINK