Cognitive diagnosis (CD) plays a crucial role in intelligent education, evaluating students' comprehension of knowledge concepts based on their test histories. However, current CD methods often model students, exercises, and knowledge concepts solely on their ID relationships, neglecting the abundant semantic relationships present within the educational data space. Furthermore, contemporary intelligent tutoring systems (ITS) frequently involve the addition of new students and exercises, creating cold-start scenarios that ID-based methods find challenging to manage effectively. The advent of large language models (LLMs) offers the potential for overcoming this challenge with open-world knowledge. In this paper, we propose LLM4CD, which Leverages Large Language Models for open-world knowledge Augmented Cognitive Diagnosis. Our method utilizes the open-world knowledge of LLMs to construct cognitively expressive textual representations, which are then encoded to introduce rich semantic information into the CD task. Additionally, we propose an innovative bi-level encoder framework that models students' test histories through two levels of encoders: a macro-level cognitive text encoder and a micro-level knowledge state encoder. This approach substitutes traditional ID embeddings with semantic representations, enabling the model to accommodate new students and exercises with open-world knowledge and address the cold-start problem. Extensive experimental results demonstrate that LLM4CD consistently outperforms previous CD models on multiple real-world datasets, validating the effectiveness of leveraging LLMs to introduce rich semantic information into the CD task.

Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Weiming Zhang, Qingyao Li, Xinyi Dai, Jizheng Chen, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Educational recommendation seeks to suggest knowledge concepts that match a learner's ability, thus facilitating a personalized learning experience. In recent years, reinforcement learning (RL) methods have achieved considerable results by taking the encoding of the learner's exercise log as the state and employing an RL-based agent to make suitable recommendations. However, these approaches suffer from handling the diverse and dynamic learner's knowledge states. In this paper, we introduce the privileged feature distillation technique and propose the P rivileged K nowledge S tate D istillation (PKSD ) framework, allowing the RL agent to leverage the "actual'' knowledge state as privileged information in the state encoding to help tailor recommendations to meet individual needs. Concretely, our PKSD takes the privileged knowledge states together with the representations of the exercise log for the state representations during training. And through distillation, we transfer the ability to adapt to learners to aknowledge state adapter. During inference, theknowledge state adapter would serve as the estimated privileged knowledge states instead of the real one since it is not accessible. Considering that there are strong connections among the knowledge concepts in education, we further propose to collaborate the graph structure learning for concepts into our PKSD framework. This new approach is termed GEPKSD (Graph-Enhanced PKSD). As our method is model-agnostic, we evaluate PKSD and GEPKSD by integrating them with five different RL bases on four public simulators, respectively. Our results verify that PKSD can consistently improve the recommendation performance with various RL methods, and our GEPKSD could further enhance the effectiveness of PKSD in all the simulations.

Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.

Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning $(\text{S}^{3}$ AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.