Motivation is only a small part of a student’s agency (Knight & Barbera, 2018). Agency plays an important role in all teaching-and-learning processes, as students are the learning subject, setting their own goals and/or accepting or rejecting goals set by others for them. Of course, students do not just determine the goals but also the paths on which they reach them. The intricate balance of self-determination (autonomy) and other-determination (heteronomy) in education can be tipped by the habitual use of GenAI tools. The speed of text generation, for example, which is so much faster than that of human writers in general and second-language learners in particular, means that texts are “ready” even before a learner could have planned them. Currently, GenAI users who have writing experience without GenAI, can check and, if necessary, adapt or even correct the computer output. With the increasing frequency and ease of GenAI use, fewer people will be able to rely on this Kulturtechnik (Wikipedia contributors, 2024) – translated as ‘cultural technology’. The German word Kulturtechnik captures technologies and established practices which are committed to frame a culture. Kulturtechniken such as writing, proof-reading, and fact-checking might deteriorate in some populations, because they are not necessarily part of the GenAI generation process. To abdicate such processes to the machine habitually – because it is easier, quicker, and more convenient – will shift the learning from learning to write in another language, for example, to learning to rely on machine output blindly.
Although learner agency has always been central in education in general and in human-computer interaction specifically, it is important for teachers to forefront it now again, especially when young or vulnerable learners interact with these most powerful GenAI tools. This way they learn that humans are ultimately responsible for the output of a machine and should therefore be able retain control over the text generation, by also being able to produce good-quality texts without copying or generating and by being able to fact-check and proof-read the textual output of GenAI tools. This requires the teacher to motivate learners, by setting and explaining appropriate goals and getting learners to accept such goals for themselves. Arguably a best practice to lay the ground for this motivation and the empowering of students’ agency is the teachers’ transparent and ethical use of GenAI tools for their lesson plans, authentic reading texts, quiz questions, and assessment rubrics as well as in their own personal use.
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.
References
Knight, J., & Barbera, E. (2018). Navigational Acts and Discourse: Fostering Learner Agency in Computer-Assisted Language Learning. The Electronic Journal of e-Learning, 16(1), 67–76.
Wikipedia contributors. (2024). Kulturtechnik. In Wikipedia. https://de.wikipedia.org/wiki/Kulturtechnik
