Yuan-Hao Jiang

East China Normal University PhD student at East China Normal University (ECNU)
Shanghai Jiao Tong University Joint doctoral student at Shanghai Jiao Tong University (SJTU)

Hello! I am a PhD candidate at the Shanghai Institute of Artificial Intelligence for Education, East China Normal University, majoring in AI for Education. Here, I lead a Fundamental Research Funds for the Central Universities and am expected to graduate in July 2027. At the same time, I am also a joint doctoral student at Shanghai Jiao Tong University. My research interests include AI for education, agentic workflow, human-computer interaction, and multimodal large language models.

Currently, I am a member of the Association for Computing Machinery (ACM), the Association for the Advancement of Computing in Education (AACE), and the ACM Special Interest Group on Computer-Human Interaction (SIGCHI). I also serve as a reviewer for ESWA, EAAI, ICLR, AIED, and other Top SCI journals or leading international conferences.

Google Scholar GitHub ORCID ResearchGate ResearchGate
Education
  • Shanghai Jiao Tong University

    Shanghai Jiao Tong University

    Joint doctoral student 2024 - 2025

  • East China Normal University

    East China Normal University

    PhD student in AI for Education 2023 - 2027

  • Jiangsu University of Science and Technology

    Jiangsu University of Science and Technology

    Master of Engineering in Pattern Recognition and Intelligent Systems 2020 - 2023


Service
Honors & Awards
  • 🏅ECNU Academic Innovation Promotion Program for Excellent Doctoral Students 2025
  • 🥈National Silver Award, China International College Students' Innovation Competition 2025
  • 🥉National Bronze Award, "Chuang Qingchun" China Youth Innovation and Entrepreneurship Competition (Technological Innovation) 2024
  • 🥈National Silver Award, China Graduate Electronics Design Contest 2023
  • 🎫Excellence Award, "Chuangqingchun" China Youth Innovation and Entrepreneurship Competition (Technological Innovation) 2023
  • 🥉National Bronze Award, "Chuangqingchun" China Youth Innovation and Entrepreneurship Competition (Digital Economy) 2023
  • 🎓Outstanding Graduate 2023
  • 🏅"Huawei Cup" China Graduate AI Innovation Competition 2023
  • 🏆National Scholarship 2022
  • 🎫Outstanding Graduate Student Model 2022
  • 🥈National Silver Award, China Graduate Electronics Design Contest 2022
  • 🥇First Prize, National Marine Vehicle Design and Production Competition 2022
  • 🥉Third Prize, "Challenge Cup" National Undergraduate Curricular Academic Science and Technology Competition 2022
  • 🎫Outstanding Graduate Student Model 2021
  • 🥇First Prize, Asia and Pacific Mathematical Contest in Modeling 2020
  • 🏆National Scholarship 2019
  • 🎫National Endeavor Scholarship 2018
News
2025
🏆 The ECNU Academic Innovation Promotion Program for Excellent Doctoral Students, which I proposed and lead, has been officially funded by ECNU! This is the only selected project from the Shanghai Institute of Artificial Intelligence for Education.
May 30
📰 Our team’s latest research on multimodal learning analytics has been accepted for publication at CHI, the most prestigious conference in the field of Human-Computer Interaction! Paper Link
Apr 25
2024
🧑‍🏫 Our latest research on multimodal mathematics dialog-based tutoring has been accepted at NeurIPS Workshop 2024! Paper Link
Dec 14
Selected Publications (view all )
TVC 2025
MAS-KCL: Knowledge Component Graph Structure Learning with Large Language Model-Based Agentic Workflow
MAS-KCL: Knowledge Component Graph Structure Learning with Large Language Model-Based Agentic Workflow
SCI Q2 CCF-C EI-Indexed Journal
Yuan-Hao Jiang, Kezong Tang, Zi-Wei Chen, Yuang Wei, Tian-Yi Liu, Jiayi Wu
Journal The Visual Computer, 2025
PDF HTML CODE/DATASET

Knowledge components (KCs) are the fundamental units of knowledge in education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph helps educators identify the root causes of learners’ poor performance on specific KCs, enabling targeted instructional interventions. We developed MAS-KCL, a KC graph structure learning algorithm that uses a multi-agent system driven by large language models for adaptive optimization of the KC graph. A bidirectional feedback mechanism is integrated to assess the value of edges and optimize graph structure learning efficiency. We validated this approach on both synthetic and real-world educational datasets, showing its effectiveness in learning path recognition, allowing teachers to design more targeted and effective learning plans. 
@article{2025-1_jiang_mas-kcl,
 title = {{MAS}-{KCL}: knowledge component graph structure learning with large language model-based agentic workflow},
 issn = {1432-2315},
 shorttitle = {{MAS}-{KCL}},
 url = {https://doi.org/10.1007/s00371-025-03946-1},
 doi = {10.1007/s00371-025-03946-1},
 language = {en},
 journal = {The Visual Computer},
 author = {Jiang, Yuan-Hao and Tang, Kezong and Chen, Zi-Wei and Wei, Yuang and Liu, Tian-Yi and Wu, Jiayi},
 month = may,
 year = {2025},
 }
CHI 2025
Explainable Learning Outcomes Prediction: Information Fusion Based on Grades Time-Series and Student Behaviors
Explainable Learning Outcomes Prediction: Information Fusion Based on Grades Time-Series and Student Behaviors
CCF-A core-A* THCPL A
Yuan-Hao Jiang, Zi-Wei Chen, Cong Zhao, Kezong Tang, Jicong Duan, Yizhou Zhou
Conference ACM CHI Conference on Human Factors in Computing Systems (CHI 2025), 2025
PDF HTML CODE/DATASET

Accurately and timely predicting learners’ outcomes can assist educators in making instructional decisions or interventions. This helps prevent students from falling into a vicious cycle of decreased academic achievement and increased aversion to learning, potentially leading to dropout. Data-driven models often outperform eXplainable Artificial Intelligence (XAI) models in predicting learning outcomes, yet their lack of interpretability can hinder trust from educators. Therefore, this study developed an XAI information fusion framework that not only extracts potential trends from the time series of student grades to enhance predictive performance but also mines explicit relationships between classroom behaviors and learning outcomes. This reveals the behavioral causes behind changes in grades. Furthermore, we have made public the Dataset for Predicting Outcomes from Time sequences and Student behaviors (DPOTS), and validated the effectiveness of the developed XAI information fusion framework based on DPOTS. The results indicate that, the Mean Absolute Error (MAE) of CEO-IF was reduced by an average of 26.32% compared to the baseline algorithms, and it showed a 22.63% reduction compared to the averaging-based information fusion method. The homepage for the project can be accessed at https://doi.org/10.5281/zenodo.14958102. 
@inproceedings{2025-2_jiang_explainable,
address = {New York, NY, USA},
series = {{CHI} {EA} '25},
title = {Explainable Learning Outcomes Prediction: {Information} Fusion Based on Grades Time-Series and Student Behaviors},
isbn = {979-8-4007-1395-8},
doi = {10.1145/3706599.3721212},
language = {en-US},
booktitle = {Proceedings of the {Extended} {Abstracts} of the {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
publisher = {Association for Computing Machinery},
author = {Jiang, Yuan-Hao and Chen, Zi-Wei and Zhao, Cong and Tang, Kezong and Duan, Jicong and Zhou, Yizhou},
month = apr,
year = {2025},
pages = {1--11},
}
ESWA 2025
Mitigating Reasoning Hallucination Through Multi-Agent Collaborative Filtering
Mitigating Reasoning Hallucination Through Multi-Agent Collaborative Filtering
TOP SCI Q1 IF = 7.5 CCF-C EI-Indexed Journal
Jinxin Shi, Jiabao Zhao, Xingjiao Wu, Ruyi Xu, Yuan-Hao Jiang, Liang He
Journal Expert Systems with Applications, 2025
PDF HTML CODE/DATASET

Large language models (LLMs) have demonstrated excellent performance in various natural language tasks. However, in practical applications, LLMs frequently exhibit hallucinations, generating content that deviates from instructions or facts, especially in complex reasoning tasks. Existing research has simulated real human behavior by utilizing multi-agent debate, voting, and review, enhancing the model’s reasoning capabilities. However, simple multi-agent systems have not accomplished the progressive verification of all reasoning steps. Additionally, the issues of unstable response quality and the continuous learning ability of agents have not been addressed. Therefore, in this work, we propose a Multi-agent Collaborative Filtering framework (MCF) in the form of cross-examination among agents. This aims to cross-verify each step while filtering and selecting the highest-quality responses from the response space. Additionally, to enable agents to achieve continuous learning capabilities, this paper proposes methods for the automated construction and efficient retrieval of the experience repository. Extensive experiments on ten reasoning datasets of three types (Arithmetic, Commonsense, and Symbolic) indicate that MCF can enhance the diversity of large language models, overcome hallucinations, and filter out effective responses in a rich response space. Moreover, the improvement of agents’ reasoning capabilities through the experience repository is also verified. Compared to the state-of-the-art, the method proposed in this paper shows superior performance. 
@article{2025-3_shi_mitigating,
	title = {Mitigating Reasoning Hallucination Through Multi-Agent Collaborative Filtering},
	volume = {263},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2024.125723},
	language = {en-US},
	number = {2025},
	journal = {Expert Systems with Applications},
	author = {Shi, Jinxin and Zhao, Jiabao and Wu, Xingjiao and Xu, Ruyi and Jiang, Yuan-Hao and He, Liang},
	month = mar,
	year = {2025},
	pages = {125723},
}
NeurIPS 2024
Synchronizing Verbal Responses and Board Writing for Multimodal Math Instruction with LLMs
Synchronizing Verbal Responses and Board Writing for Multimodal Math Instruction with LLMs
CCF-A
Yuan-Hao Jiang, Ruijia Li, Yuang Wei, Rui Jia, Xiaobao Shao, Hanglei Hu, Bo Jiang
Conference NeurIPS'24: Conference and Workshop on Neural Information Processing Systems, the 4th Workshop on Mathematical Reasoning and AI, 2024
PDF HTML CODE/DATASET

The advancement of large language models (LLMs) has greatly facilitated math instruction, with the generated textual content serving as verbal responses to address student inquiries. However, in instructional settings, teachers often provide both verbal responses and board writing (BW) simultaneously to enhance students' knowledge construction. To address this, we introduce MathBoard, a multimodal large language model (MLLM) designed for elementary mathematics education, which progressively generates BW. Our study focuses on the provision of BW to learners, aiming to reduce their cognitive load effectively. Furthermore, MathBoard can be integrated with other approaches that enhance mathematical reasoning capabilities. An empirical study involving 34 pre-service teachers demonstrated that the multimodal interactions facilitated by MathBoard were more highly accepted and impactful across various dimensions compared to text-only interactions, significantly promoting learners' social construction of knowledge. 
@inproceedings{2024-5_jiang_synchronizing,	
	title = {Synchronizing Verbal Responses and Board Writing for Multimodal Math Instruction with LLMs},	
	booktitle = {NeurIPS'24: Conference and Workshop on Neural Information Processing Systems, the 4th Workshop on Mathematical Reasoning and AI},	
	address = {Vancouver, Canada},	
	publisher = {Neural Information Processing Systems Foundation},	
	author = {Jiang, Yuan-Hao and Li, Ruijia and Wei, Yuang and Jia, Rui and Shao, Xiaobao and Hu, Hanglei and Jiang, Bo},	
	year = {2024},	
	pages = {46--59},	
	url = {https://openreview.net/forum?id=esbIrV8N12},	
}
Nova Science Publishers
Enhancing Educational Practices: Strategies for Assessing and Improving Learning Outcomes
Enhancing Educational Practices: Strategies for Assessing and Improving Learning Outcomes
Editor
Yuang Wei, Changyong Qi, Yuan-Hao Jiang, Ling Dai
MonographNova Science Publishers: New York, USA, 2024
PDF HTML CODE/DATASET

The effective assessment of learning outcomes serves as the cornerstone of educational guidance while improving learning outcomes stands as the central objective of effective teaching. As intelligent technology continues to advance, the field of education must endeavor to develop increasingly personalized, effective, and human-centric approaches to assessing and enhancing learning outcomes. To realize this vision, this book seeks to identify educational realities, dismantle educational barriers using advanced technology, and speculate on future trajectories. Throughout this book, readers will delve into cutting-edge research about the assessment and enhancement of learning outcomes, explore the latest educational technologies for this purpose, and gain a more comprehensive understanding of future research directions. Let us collectively contribute to shaping the future of AI for education. 
@book{2024-8_wei_enhancing,	
	title = {Enhancing Educational Practices: Strategies for Assessing and Improving Learning Outcomes},	
	series = {Education in a Competitive and Globalizing World},	
	address = {New York, NY, USA},	
	publisher = {Nova Science Publishers},	
	editor = {Wei, Yuang and Qi, Changyong and Jiang, Yuan-Hao and Dai, Ling},	
	year = {2024},	
	isbn = {979-8-89530-030-5},	
	doi = {https://doi.org/10.52305/RUIG5131},	
}
EDM 2024
Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang, Bo Jiang
Conference Joint Proceedings of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops (HEXED-L3MNGET 2024) co-located with 17th International Conference on Educational Data Mining (EDM 2024), 2024
PDF HTML CODE/DATASET

A reliable knowledge structure is a prerequisite for building effective adaptive learning systems and intelligent tutoring systems. Pursuing an explainable and trustworthy knowledge structure, we propose a method for constructing causal knowledge networks. This approach leverages Bayesian networks as a foundation and incorporates causal relationship analysis to derive a causal network. Additionally, we introduce a dependable knowledge-learning path recommendation technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process. 
@inproceedings{2024-4_wei_enhancing,	
	title = {Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks},	
	booktitle = {Joint Proceedings of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops (HEXED-L3MNGET 2024) co-located with 17th International Conference on Educational Data Mining (EDM 2024)},	
	address = {Atlanta, Georgia, USA},	
	publisher = {International Educational Data Mining Society},	
	author = {Wei, Yuang and Zhou, Yizhou and Jiang, Yuan-Hao and Jiang, Bo},	
	year = {2024},	
	volume = {3840},	
	isbn = {1613-0073},	
	doi = {10.48550/arXiv.2406.17518},	
	pages = {9--17},	
	url = {https://ceur-ws.org/Vol-3840/HEXED24_paper2.pdf},	
	language = {en},	
}
AIED 2024
Generating Contextualized Mathematics Multiple-Choice Questions Utilizing Large Language Models
Generating Contextualized Mathematics Multiple-Choice Questions Utilizing Large Language Models
CAAI-A ECNU-Recommended Education Conferences
Ruijia Li, Yiting Wang, Chanjin Zheng, Yuan-Hao Jiang, Bo Jiang
Conference Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2024), 2024
PDF HTML CODE/DATASET

Applying mathematics to solve authentic question play important roles in mathematics education. How to generate high-quality multiple-choice questions that have authentic context is a great challenge. By combining multiple iterations of large language model dialogues with auxiliary external tools and the LangChain framework, this work presents a novel method for automatically generating contextualized multiple-choice mathematics questions. To check the quality of generated questions, 30 questions were randomly selected and 13 human experts were invited to rate these questions. The survey result indicates that the questions produced by the proposed method exhibit a significantly higher quality compared to those generated directly by GPT4, and are already quite comparable in performance to questions that are meticulously crafted by humans across multiple dimensions. The code is available on the project home page: https://github.com/youzizzz1028/MCQ-generation-Chain. 
@inproceedings{2024-2_li_generating,	
	title = {Generating Contextualized Mathematics Multiple-Choice Questions Utilizing Large Language Models},	
	booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2024)},	
	address = {Cham},	
	publisher = {Springer Nature Switzerland},	
	author = {Li, Ruijia and Wang, Yiting and Zheng, Chanjin and Jiang, Yuan-Hao and Jiang, Bo},	
	year = {2024},	
	isbn = {978-3-031-64315-6},	
	doi = {10.1007/978-3-031-64315-6_48},	
	pages = {494--501},	
	language = {en},	
}
EAAI 2023
A Control System of Rail-Guided Vehicle Assisted by Transdifferentiation Strategy of Lower Organisms
A Control System of Rail-Guided Vehicle Assisted by Transdifferentiation Strategy of Lower Organisms
TOP SCI Q1 IF = 7.5 CCF-C EI-Indexed Journal
Yuan-Hao Jiang, Shang Gao, Yu-Hang Yin, Zi-Fan Xu, Shao-Yong Wang
Journal Engineering Applications of Artificial Intelligence, 2023
PDF HTML CODE/DATASET

Rail-guided vehicle is a logistics management device widely used to perform various material handling operations instead of manual labor. In processing scenarios, the dimensions of the material transfer path of a rail-guided vehicle are typically very large, which makes the optimization of the material transfer path very difficult. The transdifferentiation behavior of lower organisms was introduced into the evolutionary algorithm, and a large-scale differential evolution algorithm based on the transdifferentiation strategy was proposed, for achieving high-efficiency processing. This strategy makes it possible for some individuals with poor fitness to reach maturity again and be selected for the next iteration after losing some information and returning to their juvenile stage, which helps maintain the diversity of the population. Simulation results show that the proposed algorithm not only achieves an average 25.68% higher output rate than the comparison algorithms on the test cases but also has an excellent and stable effect distribution level on the extended problem space, which shows that the superiority of the proposed algorithm is not affected by the processing parameters. This research is expected to provide technical guidance for the processing of key components in the ship and aviation manufacturing industries. 
@article{2023-1_jiang_control,	
	title = {A Control System of Rail-Guided Vehicle Assisted by Transdifferentiation Strategy of Lower Organisms},	
	volume = {123},	
	issn = {0952-1976},	
	doi = {doi.org/10.1016/j.engappai.2023.106353},	
	language = {en-US},	
	journal = {Engineering Applications of Artificial Intelligence},	
	author = {Jiang, Yuan-Hao and Gao, Shang and Yin, Yu-Hang and Xu, Zi-Fan and Wang, Shao-Yong},	
	month = may,	
	year = {2023},	
	pages = {106353},	
}
All publications