An Adaptive Alignment Method for Learning Path Generation and Learning Objectives Based on the IB-GRPO Large Language Model

Abstract

Large language models used in personalized education often face mismatches between generated learning paths and teaching objectives, limited adaptive generation ability, and insufficient modeling of heterogeneous learner cognitive profiles. This paper proposes an adaptive alignment method for learning path generation based on the IB-GRPO large language model. First, a multidimensional feature space is constructed by integrating a knowledge graph, learner profiles, and teaching objectives. A knowledge-node importance assessment module dynamically assigns weights to learning content. Second, learners are clustered according to cognitive levels, and a group relative policy optimization algorithm updates path strategies within each group. Third, a teaching-objective constraint function is introduced, and reinforcement learning is used to realize adaptive alignment between generated paths and learning objectives. Finally, a feedback loop dynamically adjusts path-generation strategies according to learning results. Experiments on a dataset of 5,000 learners show that the method achieves a target coverage rate of 90.8% and an ability matching rate of 93.1%. The results demonstrate that the grouping strategy effectively distinguishes learners’ needs and improves personalized path generation.