Personalized lesson planning is time-consuming and demands simultaneous alignment to curriculum standards, classroom constraints, and individual teacher style. Although large language models (LLMs) can draft lesson plans, their outputs often remain generic, lack verifiable grounding in official curricula, and require substantial teacher revision to become classroom-ready. We present TeachPlanAlign, a dual-profile lesson plan generation framework that (i) models both teacher preferences and class learning context, (ii) grounds generation in curriculum evidence through retrieval-augmented fine-tuning designed for open-book generation, and (iii) iteratively improves pedagogical coherence via a constraint-guided self-refinement loop. The framework produces structured plans with explicit time allocation, differentiation strategies, and an evidence-linked rationale that supports traceability to standards and instructional resources. We evaluate TeachPlanAlign on a multi-subject benchmark of lesson requests paired with curriculum documents and human-authored plans, and we further validate its usability through teacher-in-the-loop evaluation. Results show consistent improvements in curriculum alignment, evidence faithfulness, and teacher preference satisfaction, while reducing teacher editing effort. These findings suggest that dual-profile alignment with traceable curriculum grounding can improve the usability and auditability of LLM-based lesson planning on this benchmark, but they should not yet be interpreted as evidence of deployment readiness.
Fu et al. (Wed,) studied this question.