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Lesson planning is a core professional practice for pre-service teachers, yet opportunities for timely, individualized feedback are frequently constrained by educator workload. While generative AI has the potential to enhance planning processes and expand opportunities for individualized feedback, the provision of comprehensive lesson plans may lead to excessive reliance. This conceptual design paper details the development and theoretical underpinnings of an artificial intelligence-assisted feedback tool that provides self-efficacy-strengthening feedback on lesson plans for pre-service teachers. To promote constructive feedback, the AI-assisted feedback tool integrates principles from educational feedback research and structures feedback to foster teachers’ lesson-planning self-efficacy through mastery-oriented affirmations, vicarious examples, social persuasions, and emotional reassurance. Curriculum alignment is incorporated to support content validity and contextual appropriateness. While the initial implementation of the feedback tool focuses on Western Australian teacher education, an explicit transfer perspective is considered for the German vocational education context. The paper describes the iterative development process that follows a design-based research approach including platform evaluation, internal refinement, and expert review by teacher educators in Western Australia. The resulting system prompt architecture comprises 11 dimensions including general baselines, the interaction between the Lesson Planning Coach and PSTs and the theoretical foundations mentioned above. The tools’ environment, including examples for provided feedback on lesson plans, is presented and discussed. Finally, an outlook is given on the planned empirical research to evaluate the effectiveness of the tool.
Vernholz et al. (Fri,) studied this question.