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Rui Lv

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3 papers

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AAAI Conference 2025 Conference Paper

Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems

Weibo Gao
Qi Liu
Linan Yue
Fangzhou Yao
Rui Lv
Zheng Zhang
Hao Wang
Zhenya Huang

Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the scarcity of offline practice response data (e.g., answer correctness) and potential biases in human online practice create a significant gap between offline metrics and the actual online performance of personalized learning services. To address this challenge, we introduce Agent4Edu, a novel personalized learning simulator leveraging recent advancements in human intelligence through large language models (LLMs). Agent4Edu features LLM-powered generative agents equipped with learner profile, memory, and action modules tailored to personalized learning algorithms. The learner profiles are initialized using real-world response data, capturing practice styles and cognitive factors. Inspired by psychology theory, the memory module records practice facts and high-level summaries, integrating reflection mechanisms. The action module supports various behaviors, including exercise understanding, analysis, and response generation. Each agent can interact with personalized learning algorithms, such as computerized adaptive testing, enabling a multifaceted evaluation and enhancement of customized services. Through a comprehensive assessment, we explore the strengths and weaknesses of Agent4Edu, emphasizing the consistency and discrepancies in responses between agents and human learners.

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JBHI Journal 2025 Journal Article

Gated-STGFormer: Spatiotemporal Fusion Network for Reconstructing Aortic Valve Motion Within Coronary Presence

Peng Shu
Daochun Li
Rui Lv
Yongkang Lee
Lingqi Kong
Shiwei Zhao
Jinwu Xiang

Accurately predicting aortic valve movement under coronary influence is critical for personalized cardiac interventions and virtual surgery planning. Conventional Fluid-Structure Interaction (FSI) models tend to neglect modeling coronary arteries due to their complexities, leading to bias in leaflet motion simulations. To overcome this limitation, we propose a combination of spatiotemporal graph convolution and Transformer based gated (Gated-STGFormer) deep learning framework, which learns to reconstruct coronary-modulated leaflet motion from simulations without explicit coronary arteries. The framework integrates Graph Convolutional Networks (GCNs) for spatial dependency modeling and Transformer for temporal dependency, with both encoding and gating mechanisms to effectively capture spatiotemporal couplings. Quantitative evaluation demonstrated that it reproduces the spatiotemporal movements of leaflets under the coronary arteries with a high degree of fidelity. By addressing anatomical simplifications in conventional simulations, this method provides a physics-informed and computationally efficient surrogate model with strong clinical applicability. Our findings suggest that the Gated-STGFormer effectively incorporates spatiotemporal modal information, and can serve as a module for coronary artery compensation in a preoperative planning system, enabling more realistic and personalized valve biomechanical simulations.

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AAAI Conference 2025 Conference Paper

GenAL: Generative Agent for Adaptive Learning

Rui Lv
Qi Liu
Weibo Gao
Haotian Zhang
Junyu Lu
Linbo Zhu

Adaptive learning, also known as adaptive teaching, relies on learning path recommendations that sequentially suggest personalized learning items (such as lectures and exercises) to meet the unique needs of each learner. Despite the extensive research in this field, previous approaches have primarily modeled the interaction sequences between learners and items using simple indexing, leading to three issues: (1) The utilization of information from both learners and items is not sufficient. For instance, these models are unable to leverage the semantic information contained within the textual content of the items. (2) Models need to be retrained on different datasets separately, which makes it difficult to adapt to the continuously expanding item pool in online educational scenarios. (3) The existing recommendation paradigm based on trained reinforcement learning frameworks, suffers from unstable recommendation performance in sparse learning logs. To address these challenges, we propose a generalized Generative Agent for Adaptive Learning (GenAL), which integrates educational tools with LLMs' semantic understanding to enable effective and generalizable learning path recommendations across diverse data distributions. Specifically, our framework consists of two components: the Global Thinking Agent, which updates the learner profile and reflects on recommendation outcomes based on the learner's historical learning records. The other is the Local Teaching Agent, which recommends items using educational prior knowledge. Leveraging the LLM's robust semantic understanding, our framework does not rely on item indexing but instead extracts relevant information from the textual content. We evaluated our approach on three real-world datasets, and the experimental results demonstrate that our GenAL not only consistently outperforms all baselines but also exhibits strong generalization ability.

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