BETWEEN DELEGATING AND DECIDING: SELF-REGULATION AND TEACHER AUTONOMY IN SCIENCE LESSON PLANNING WITH GENERATIVE ARTIFICIAL INTELLIGENCE

Abstract

This chapter discusses lesson planning with generative artificial intelligence in the initial education of science and biology teachers, focusing on the tension between delegating pedagogical decisions to the tool and making them deliberately. It is a theoretical, qualitative study developed as a reflective theoretical essay. Its aim is to examine under which conditions the use of this technology strengthens, rather than weakens, the autonomy of future teachers, in light of the social cognitive theory of self-regulation and the notion of teacher autonomy. The methodology relied on a narrative literature review, with an intentional selection of reference works on self-regulation, teacher knowledge and teacher autonomy, empirical studies published between 2023 and 2026 on artificial intelligence in lesson planning and teacher education, and policy documents, organized into analytical axes. The discussion indicates that the plans generated by the tool vary in quality and require a pedagogical judgment that preservice teachers are still building; that delegation can improve the product without developing the person who plans; and that writing the commands addressed to the tool makes the forethought, performance and self-reflection phases of planning observable. As a synthesis, the study proposes a continuum of stances, from commissioning to critical co-authorship, and a set of guiding questions for each phase of the self-regulatory cycle. It concludes that initial teacher education needs to treat these commands as objects of pedagogical reflection, so that technology expands, rather than replaces, teacher authorship.