R e AL : How Can LLM s Simulate the Real Teacher? Retrieval-enhanced Agent for Adaptive Learning (2025)

Abstract: Adaptive learning focuses on recommending personalized materials (e.g., exercises, courses) to the unique needs of learners. Despite significant research, these methods still lag behind real teachers including two main limitations: (1) Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information, particularly the inability to leverage semantic content from item text; (2) The data-driven reinforcement learning frameworks struggle with stable performance in scenarios with sparse learning logs. To address these challenges, we introduce the Retrieval-enhanced Agent for Adaptive Learning (ReAL) powered by large language models (LLMs), to simulate teacher decision-making with extensive prior knowledge and teaching experience. Specifically, we approach the simulation from both internal and external perspectives. From the internal perspective, we utilize the superior natural language standing ability of LLMs to analyze item texts and learner profiles. This mechanism contributes to the generation of personalized and appropriate item candidates. From the external perspective, we simulate the teacher experience by retrieving similar learners, further ensuring the model’s performance on sparse interaction data. Furthermore, we design a reflector based on learners’ feedback to refine the recommendation process. Evaluation on three real-world datasets demonstrates the superiority of ReAL in both data utilization, recommendation accuracy and stability compared to various representative baselines.

## introduction unlike traditional classroom teaching, which provides the same materials for all learners, adaptive learning offers personalized tasks (e.g., exercises) and pathways. this makes adaptive learning a more efficient approach @xcite @xcite ## problem definition following @xcite , we focus on step-by-step recommendations for sessionbased learning paths based on real-time interactions. a learner's process typically involves two types of items: learning items (e.g., knowledge concepts or skills) and exercise items (e.g., questions). without loss of generality, we denote the knowledge concept item set as @xmath0, k 2 , . . . , k m } and the exercises item set as @xmath1, e 2 , . . .}. instead of only using item index in previous work, the k @xmath2. the learner's goals are denoted as @xmath3, g 2 , . . . , g m }, where g @xmath4. the learning process is as follows: before starting, educational tools test a learner on their goals to obtain an initial score e s . the learning path is generated step-by-step as @xmath5, e 2 , . . . , e p }, where e @xmath6, aligning with previous works. after completing the entire learning path, a final test using educational tools (e.g., knowledge tracing models) is taken on the learning goals to obtain a final score e e , allowing to calculate learning effectiveness e p : ## real framework our real, shown in figure 2 , consists of three modules: planner, actor, and reflector. the planner analyzes the current learner from an internal perspective. it leverages llms' semantic understanding and reasoning abilities, along with educational tools, to recommend a set of candidate exercises. the actor simulates an experienced teacher from an external perspective. it selects the most suitable item for the learner by considering the performance of similar past learners on the candidate items. finally, the reflector updates the recommendation strategy in the planner based on feedback. ## conclusion in this paper, we introduced the retrieval-enhanced agent for adaptive learning (real) framework, which addresses key limitations in existing adaptive learning methods. prior approaches have largely focused on learner-item interactions based on id sequences, failing to fully utilize both learner and item information, especially the semantic content of items. additionally, data-driven reinforcement learning methods struggle in scenarios with sparse learning logs. real, powered by large language models (llms), simulates teacher decisionmaking from both internal and external perspectives. internally, real leverages llms' semantic understanding and prior knowledge to analyze item texts and learner profiles, generating item candidates. externally, it retrieves similar learners to select the most suitable item. moreover, the recommendation strategy is continuously refined through learner feedback using a reflection mechanism. by moving beyond item indexing and relying on semantic content, real demonstrates robust and superior performance across three real-world datasets, particularly in sparse data conditions.