Weiqi Luo

AAAI Conference 2026 Conference Paper

GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

• Xinghe Cheng
• Zihan Zhang
• Jiapu Wang
• Liangda Fang
• Chaobo He
• Quanlong Guan
• Shirui Pan
• Weiqi Luo

Learning path recommendation seeks to provide students with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relations, which present two major limitations: (1) Prerequisite relations between knowledge concepts are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. (2) Relying on a single sequentially dependent knowledge structure based on prerequisite relations implies that a confusing knowledge concept can disrupt subsequent learning processes, which is referred to as blocked learning. To address these two challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relations between knowledge concepts. Specifically, we introduce a knowledge structure graph generation module EDU-GraphRAG that constructs knowledge structure graphs for different educational datasets, significantly improving the applicability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module that utilizes similarity relations as fallback relations when prerequisite relations become ineffective, thereby alleviating the blocked learning. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also generates more effective and longer learning paths.

AAAI Conference 2026 Conference Paper

Learning from Scoring Disagreements: Contrastive Error Mining for Efficient and Robust LLM-based Assessment

• Lei Chen
• Tengteng Cheng
• BoYu Gao
• Zitao Liu
• Weiqi Luo

Automated grading of student responses still faces numerous challenges, particularly when dealing with complex and ambiguous answers. In particular, large models are prone to scoring bias when handling uncertain responses, and few-shot reasoning methods often lack stability, which limits their applicability in real educational scenarios. To tackle these challenges, we propose the Contrastive Error Mining and FineTuning (CEM-FT) framework, which automatically identifies high-value hard samples by analyzing scoring disagreements between a full fine-tuned model and a few-shot model. A lightweight LoRA adapter is then trained on these samples to refine model performance with minimal computational overhead. Experiments on the SciEntsbank, Beetle, and Mohler datasets show that CEM-FT can improve QWK by up to 3.9% compared to the fine-tuned Qwen model on SciEntsbank datasets, which is a significant improvement over the few-shot baseline. The proposed framework substantially enhances both scoring accuracy and consistency, providing a practical, robust solution for reliable automated assessment with large language models.

EAAI Journal 2025 Journal Article

A large-scale mobile application knowledge graph for the research of cybersecurity: Construction and application

• Weizhuo Li
• Heng Zhou
• Yiming Tan
• Weiqi Luo
• Qiu Ji
• Yuyang Bian

AAAI Conference 2025 Conference Paper

Cognitive Fluctuations Enhanced Attention Network for Knowledge Tracing

• Mingliang Hou
• Xueyi Li
• Teng Guo
• Zitao Liu
• Mi Tian
• Renqiang Luo
• Weiqi Luo

Knowledge tracing (KT) involves using the historical records of student-learning interactions to anticipate their performance on forthcoming questions. Central to this process is the modeling of human cognition to gain deeper insights into how knowledge is acquired and retained. Human cognition is characterized by two key features: long-term cognitive trends, reflecting the gradual accumulation and stabilization of knowledge over time, and short-term cognitive fluctuations, which arise from transient factors such as forgetting or momentary lapses in attention. Although existing attention-based KT models effectively capture long-term cognitive trends, they often fail to adequately address short-term cognitive fluctuations. These limitations lead to overly smoothed cognitive features and reduced model performance, especially when the test data length exceeds the training data length. To address these problems, we propose FlucKT, a novel short-term cognitive fluctuations enhanced attention network for KT tasks. FlucKT improves the attention mechanism in two ways: First, by using a decomposition-based layer with causal convolution to separate and dynamically reweight long-term and short-term cognitive features. Second, by introducing a kernelized bias attention score penalty to enhance focus on short-term fluctuations, improving length generalization capabilities. Our contributions are validated through extensive experiments on three real-world datasets, demonstrating significant improvements in length generalization and prediction performance.

IJCAI Conference 2025 Conference Paper

Denoised Attention and Question-Augmented Representations for Knowledge Tracing

• Jiwei Deng
• Youheng Bai
• Mingliang Hou
• Teng Guo
• Zitao Liu
• Weiqi Luo

Knowledge tracing (KT) is an essential task in online education systems. It aims to predict the future performance of students based on their historical learning interaction data. Despite significant advancements in attention-based KT models, they still face some limitations: inaccurate input representation and excessive student forgetting modeling. These limitations often lead to the attention noise problem: the model assigns non-negligible attention weight to some information that is cognitively irrelevant in nature, thereby generating interference signals. To address this problem, we propose a novel KT model, i. e. , DenoiseKT. DenoiseKT effectively models the difficulty of the questions and utilizes graph neural network to capture the complex relationship between questions, thereby refining the representations of input features. Additionally, the denoised attention mechanism introduces a weight factor to reduce the model's attention weight distribution on irrelevant information. We extensively compare DenoiseKT with 22 state-of-the-art KT models on 4 widely-used public datasets. Experimental results show that DenoiseKT can effectively solve the attention noise problem and outperform other models. The source code of DenoiseKT is available at https: //pykt. org.

AAAI Conference 2025 Conference Paper

Rethinking and Improving Student Learning and Forgetting Processes for Attention based Knowledge Tracing Models

• Youheng Bai
• Xueyi Li
• Zitao Liu
• Yaying Huang
• Mi Tian
• Weiqi Luo

Knowledge tracing (KT) models students' knowledge states and predicts their future performance based on their historical interaction data. However, attention based KT models struggle to accurately capture diverse forgetting behaviors in ever-growing interaction sequences. First, existing models use uniform time decay matrices, conflating forgetting representations with problem relevance. Second, the fixed-length window prediction paradigm fails to model continuous forgetting processes in expanding sequences. To address these challenges, this paper introduces LefoKT, a unified architecture that enhances attention based KT models by incorporating proposed relative forgetting attention. LefoKT improves forgetting modeling through relative forgetting attention to decouple forgetting patterns from problem relevance. It also enhances attention based KT models' length extrapolation capability for capturing continuous forgetting processes in ever-growing interaction sequences. Extensive experimental results on three datasets validate the effectiveness of LefoKT.

AAAI Conference 2025 Conference Paper

What Are Step-Level Reward Models Rewarding? Counterintuitive Findings from MCTS-Boosted Mathematical Reasoning

• Yiran Ma
• Zui Chen
• Tianqiao Liu
• Mi Tian
• Zhuo Liu
• Zitao Liu
• Weiqi Luo

Step-level reward models (SRMs) can significantly enhance mathematical reasoning performance through process supervision or step-level preference alignment based on reinforcement learning. The performance of SRMs is pivotal, as they serve as critical guidelines, ensuring that each step in the reasoning process is aligned with desired outcomes. Recently, AlphaZero-like methods, where Monte Carlo Tree Search (MCTS) is employed for automatic step-level preference annotation, have proven particularly effective. However, the precise mechanisms behind the success of SRMs remain largely unexplored. To address this gap, this study delves into the counterintuitive aspects of SRMs, particularly focusing on MCTS-based approaches. Our findings reveal that the removal of natural language descriptions of thought processes has minimal impact on the efficacy of SRMs. Furthermore, we demonstrate that SRMs are adept at assessing the complex logical coherence present in mathematical language while having difficulty in natural language. These insights provide a nuanced understanding of the core elements that drive effective step-level reward modeling in mathematical reasoning. By shedding light on these mechanisms, this study offers valuable guidance for developing more efficient and streamlined SRMs, which can be achieved by focusing on the crucial parts of mathematical reasoning.

IJCAI Conference 2024 Conference Paper

Enhancing Length Generalization for Attention Based Knowledge Tracing Models with Linear Biases

• Xueyi Li
• Youheng Bai
• Teng Guo
• Zitao Liu
• Yaying Huang
• Xiangyu Zhao
• Feng Xia
• Weiqi Luo

Knowledge tracing (KT) is the task of predicting students' future performance based on their historical learning interaction data. With the rapid advancement of attention mechanisms, many attention based KT models are developed. However, existing attention based KT models exhibit performance drops as the number of student interactions increases beyond the number of interactions on which the KT models are trained. We refer to this as the length generalization of KT model. In this paper, we propose stableKT to enhance length generalization that is able to learn from short sequences and maintain high prediction performance when generalizing on long sequences. Furthermore, we design a multi-head aggregation module to capture the complex relationships between questions and the corresponding knowledge components (KCs) by combining dot-product attention and hyperbolic attention. Experimental results on three public educational datasets show that our model exhibits robust capability of length generalization and outperforms all baseline models in terms of AUC. To encourage reproducible research, we make our data and code publicly available at https: //pykt. org.

IJCAI Conference 2024 Conference Paper

On the Logic of Theory Change Iteration of KM-Update, Revised

• Liangda Fang
• Tong Zhu
• Quanlong Guan
• Junming Qiu
• Zhao-Rong Lai
• Weiqi Luo
• Hai Wan

Belief revision and update, two significant types of belief change, both focus on how an agent modifies her beliefs in presence of new information. The most striking difference between them is that the former studies the change of beliefs in a static world while the latter concentrates on a dynamically-changing world. The famous AGM and KM postulates were proposed to capture rational belief revision and update, respectively. However, both of them are too permissive to exclude some unreasonable changes in the iteration. In response to this weakness, the DP postulates and its extensions for iterated belief revision were presented. Furthermore, Ferme and Goncalves integrated these postulates in belief update. Unfortunately, some redundant components are included in the definitions of belief states and the faithful assignments for semantic characterizations. Moreover, their approach does not meet the desired property of iterated belief update. They also do not discuss the rationale of any DP postulate within the update context. This paper is intended to fix these deficiencies of Ferme and Goncalves’s approach. Firstly, we present a modification of the original KM postulates based on belief states, and propose the notion of faithful collective assignments of belief states to partial preorders. Subsequently, we migrate several well-known postulates for iterated belief revision to iterated belief update. Moreover, we provide the exact semantic characterizations based on partial preorders for each of the proposed postulates. Finally, we analyze the compatibility between the above iterated postulates and the KM postulates for belief update.

AAAI Conference 2024 Conference Paper

SDGAN: Disentangling Semantic Manipulation for Facial Attribute Editing

• Wenmin Huang
• Weiqi Luo
• Jiwu Huang
• Xiaochun Cao

Facial attribute editing has garnered significant attention, yet prevailing methods struggle with achieving precise attribute manipulation while preserving irrelevant details and controlling attribute styles. This challenge primarily arises from the strong correlations between different attributes and the interplay between attributes and identity. In this paper, we propose Semantic Disentangled GAN (SDGAN), a novel method addressing this challenge. SDGAN introduces two key concepts: a semantic disentanglement generator that assigns facial representations to distinct attribute-specific editing modules, enabling the decoupling of the facial attribute editing process, and a semantic mask alignment strategy that confines attribute editing to appropriate regions, thereby avoiding undesired modifications. Leveraging these concepts, SDGAN demonstrates accurate attribute editing and achieves high-quality attribute style manipulation through both latent-guided and reference-guided manners. We extensively evaluate our method on the CelebA-HQ database, providing both qualitative and quantitative analyses. Our results establish that SDGAN significantly outperforms state-of-the-art techniques, showcasing the effectiveness of our approach. To foster reproducibility and further research, we will provide the code for our method.

AAAI Conference 2023 Conference Paper

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

• Jiahao Chen
• Zitao Liu
• Shuyan Huang
• Qiongqiong Liu
• Weiqi Luo

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

NeurIPS Conference 2023 Conference Paper

XES3G5M: A Knowledge Tracing Benchmark Dataset with Auxiliary Information

• Zitao Liu
• Qiongqiong Liu
• Teng Guo
• Jiahao Chen
• Shuyan Huang
• Xiangyu Zhao
• Jiliang Tang
• Weiqi Luo

Knowledge tracing (KT) is a task that predicts students' future performance based on their historical learning interactions. With the rapid development of deep learning techniques, existing KT approaches follow a data-driven paradigm that uses massive problem-solving records to model students' learning processes. However, although the educational contexts contain various factors that may have an influence on student learning outcomes, existing public KT datasets mainly consist of anonymized ID-like features, which may hinder the research advances towards this field. Therefore, in this work, we present, \emph{XES3G5M}, a large-scale dataset with rich auxiliary information about questions and their associated knowledge components (KCs)\footnote{\label{ft: kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill. }. The XES3G5M dataset is collected from a real-world online math learning platform, which contains 7, 652 questions, and 865 KCs with 5, 549, 635 interactions from 18, 066 students. To the best of our knowledge, the XES3G5M dataset not only has the largest number of KCs in math domain but contains the richest contextual information including tree structured KC relations, question types, textual contents and analysis and student response timestamps. Furthermore, we build a comprehensive benchmark on 19 state-of-the-art deep learning based knowledge tracing (DLKT) models. Extensive experiments demonstrate the effectiveness of leveraging the auxiliary information in our XES3G5M with DLKT models. We hope the proposed dataset can effectively facilitate the KT research work.

NeurIPS Conference 2022 Conference Paper

pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

• Zitao Liu
• Qiongqiong Liu
• Jiahao Chen
• Shuyan Huang
• Jiliang Tang
• Weiqi Luo

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKT methods via thorough evaluations. The \textsc{pyKT} library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e. g. , wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and our experimental results at \url{https: //pykt. org/}. We welcome contributions from other research groups and practitioners.