Evaluating Generative AI Assisted Lesson Planning for Secondary Chemistry: A Critical Review of Planning Quality and Teacher Judgment

Abstract

Generative artificial intelligence (GenAI) can produce chemistry lesson plans containing objectives, activities, resources, and assessments, but evaluating their educational contribution requires different kinds of evidence. This critical narrative review examines GenAI-assisted planning for secondary chemistry through three objects of evaluation: the written plan, teachers' professional reasoning and revisions, and students' learning. Seven recent chemistry and science education studies provide the principal contemporary evidence, interpreted alongside research on pedagogical content knowledge, chemical representations, and constructive alignment. The synthesis identifies useful planning support while showing why completed templates, teacher perceptions, and classroom outcomes require separate interpretation. Chemistry-specific review should examine the conditions attached to scientific claims, the connections among representations, the reasoning elicited by tasks, and the responses that trigger instructional adjustment. A worked illustration using electrolysis of aqueous copper sulfate demonstrates how these criteria translate into changes to objectives, diagnostic questions, and follow-up teaching decisions. The article proposes a planning-review procedure and a teacher-education assignment that make justified revisions visible through planning records and discussion. Future studies can connect these records with classroom enactment and student explanations while separating AI assistance from additional opportunities to revise. The review positions teachers' transformation of generated materials as the link between the availability of planning support and the quality of the learning opportunities eventually offered to students.