With the rapid development of Large Language Models （LLMs）， dialogue assistants based on LLM have emerged as a new learning method for students. These assistants generate answers through interactive Q&A， helping students solve problems and improve learning efficiency. However， the existing conversational assistants ignore students’ personalized needs， failing to provide personalized answers for “tailored instruction”. To address this， a personalized conversational assistant framework based on student capability perception was proposed， which is consisted of two main modules： a capability perception module that analyzes students’ exercise records to explore the knowledge proficiency of the students， and a personalized answer generation module that creates personalized answers based on the capabilities of the students. Three implementation paradigms — instruction-based， data-driven， and agent-based ones were designed to explore the framework’s practical effects. In the instruction-based assistant， the inference capabilities of LLMs were used to explore knowledge proficiency of the students from students’ exercise records to help generate personalized answers； in the small model-driven assistant， a Deep Knowledge Tracing （DKT） model was employed to generate students’ knowledge proficiency； in the agent-based assistant， tools such as student capability perception， personalized detection， and answer correction were integrated using LLM agent method for assistance of answer generation. Comparison experiments using Chat General Language Model （ChatGLM） and GPT4o_mini demonstrate that LLMs applying all three paradigms can provide personalized answers for students， the accuracy of the agent-based paradigm is higher， indicating the superior student capability perception and personalized answer generation of this paradigm.

Finding similar exercises aims to retrieve exercises with similar testing goals to a given query exercise from the exercise database. As online education evolves， the exercise database is growing in size， and due to the professional characteristic of the exercises， it is not easy to annotate their relations. Thus， online education systems require an efficient and unsupervised model for finding similar exercise. Unsupervised semantic hashing can map high-dimensional data to compact and efficient binary representation under the premise of unsupervised signals. However，it is inadequate to simply apply the semantic hashing model to the similar exercise retrieval model because exercise data contains rich semantic information while the representation space of binary vector is limited. To address this issue， a similar exercise retrieval model was introduced to acquire and retain crucial information. Firstly， a crucial information acquisition module was designed to acquire critical information from exercise data and a de-redundancy object loss was proposed to eliminate redundant information. Secondly， a time-aware activation function was introduced to reduce coding information loss. Thirdly， to maximize the utilization of the Hamming space， a bit balance loss and a bit independent loss were introduced to optimize the distribution of binary representation in the optimization process. Experimental results on MATH and HISTORY datasets demonstrate that the proposed model outperforms the state-of-the-art text semantic hashing model Deep Hash InfoMax （DHIM）， with an average improvement of approximately 54% and 23% respectively across three recall settings. Moreover， compared to the best-performing similar exercise retrieval model QuesCo， the proposed model demonstrates a clear advantage on search efficiency.