Developing self-regulation for generative AI through a metacognitive checklist: A case study of an in-service language teacher

Abstract

Regulatory checklists are used in many areas of education and professional development because they can support metacognition and self-regulation, two fundamental processes for lifelong-learning, which may occur in an informal and autonomous fashion. Owing to its dynamic nature, technology in education perfectly fits within the paradigm of continuous, often informal, learning for professional development, and it is consequently useful to foster self-regulation in language teachers. This research reports on a case study of an in-service teacher using a regulatory checklist during a training on generative artificial intelligence (AI) for language teaching. The case shows that the checklist was helpful in aiding the teacher to develop proficiency in AI and the high-order thinking skills required to work with it and, most of all, self-regulation. The case is of interest because the teacher was driven by practical motivations, rather than theoretical and pedagogical ones, which explains how she used the checklist; for instance, she focused more on monitoring than evaluation. Implications for teacher training are discussed.