Abstract: Efficient exercise recommendation is crucial in intelligent education to provide learners with personalized learning resources based on their cognitive states. Existing methods often rely on learners’ mastery levels of knowledge concepts, yet critically overlook contextual information embedded in response sequences data, thus limiting the enhancement of learners’ knowledge transfer abilities. This study introduces a context-aware adaptive exercise recommendation method for response sequences data (CAE-RRS). By constructing a learning context representation module that integrates knowledge concept and exercise contexts, the method offers a richer contextual representation. Furthermore, it incorporates a cognitive state representation module fused with temporal large language models (LLMs) and implements a context-aware adaptive exercise recommendation module based on knowledge graphs. This module dynamically links exercises, knowledge concepts, and learner characteristics, enabling precise and adaptive recommendations according to learning context features. It effectively addresses the challenge of context-aware adaptive recommendations, bolstering the model’s contextual perception and adaptability. Consequently, it ensures learning effectiveness while enhancing the novelty of the learning process and the efficiency and accuracy of exercise recommendation systems. Comparative and ablation experiments across the AAAI 2023 and NeurIPS_t34 datasets demonstrate that our model surpasses baseline models in both accuracy and novelty.