Harnessing LLM for Noise-Robust Cognitive Diagnosis in Web-Based Intelligent Education Systems
School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology（计算机科学与技术学院/人工智能学院，中国矿业大学）；School of Computing Technologies, RMIT University（计算技术学院，拉筹伯大学）；Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；School of Computer Science and Technology, Hainan University（计算机科学与技术学院，海南大学）
Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent work has tried to utilize Large Language Models (LLMs) for cognitive diagnosis, yet LLMs struggle with structured data and are prone to noise-induced misjudgments. Specially, WIES's open environment continuously attracts new students and produces vast amounts of response logs, exacerbating the data imbalance and noise issues inherent in traditional educational systems. To address these challenges, we propose DLLM, a Diffusion-based LLM framework for noise-robust cognitive diagnosis. DLLM first constructs independent subgraphs based on response correctness, then applies relation augmentation alignment module to mitigate data imbalance. The two subgraph representations are then fused and aligned with LLM-derived, semantically augmented representations. Importantly, before each alignment step, DLLM employs a two-stage denoising diffusion module to eliminate intrinsic noise while assisting structural representation alignment. Specifically, unconditional denoising diffusion first removes erroneous information, followed by conditional denoising diffusion based on graph-guided to eliminate misleading information. Finally, the noise-robust representation that integrates semantic knowledge and structural information is fed into existing cognitive diagnosis models for prediction. Experimental results on three publicly available web-based educational platform datasets demonstrate that our DLLM achieves optimal predictive performance across varying noise levels, which demonstrates that DLLM achieves noise robustness while effectively leveraging semantic knowledge from LLM.
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models
UCL Knowledge Lab, Institute of Education, University College London, UK（伦敦大学学院教育研究所知识实验室）；AI Centre, Dept. of Computer Science, University College London, UK（伦敦大学学院人工智能中心）
Explainable Collaborative Problem Solving Diagnosis with BERT using SHAP and its Implications for Teacher Adoption
UCL Knowledge Lab, Institute of Education, University College London, UK（伦敦大学学院教育学院知识实验室）；UCL Centre for Artificial Intelligence, Department of Computer Science, University College London, UK（伦敦大学学院人工智能中心）
Ensemble Deep Learning and LLM-Assisted Reporting for Automated Skin Lesion Diagnosis
Institute of Computer Science The University of Agriculture Peshawar, Pakistan（计算机科学研究所 伊斯兰农业大学 薩瓦爾，巴基斯坦）；Department of Computer Science Islamia College Peshawar Pakistan（计算机科学系 伊斯兰学院 薩瓦爾，巴基斯坦）；Department of Software Engineering Islamia College Peshawar Pakistan（软件工程系 伊斯兰学院 薩瓦爾，巴基斯坦）
DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Center for Healthcare Organization and Implementation Research, VA Bedford Health Care（VA贝德福德医疗保健中心医疗组织与实施研究中心）；Manning College of Information and Computer Sciences, UMass Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Miner School of Computer and Information Sciences, UMass Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）；Department of Internal Medicine, Chung-Ang University（韩国中央大学内科学系）
TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis
CCNU Wollongong Joint Institute（华中师范大学Wollongong联合学院）；Central China Normal University（华中师范大学）；Faculty of Artificial Intelligence in Education（教育人工智能学院）；Hubei University of Technology（湖北工业大学）；School of Electrical and Electronic Engineering（电气与电子工程学院）
Tell Me Why: Designing an Explainable LLM-based Dialogue System for Student Problem Behavior Diagnosis
School of Educational Technology, Beijing Normal University, Beijing, China（北京师范大学教育技术学院）；Advanced Innovation Center for Future Education, Beijing Normal University, Beijing, China（北京师范大学未来教育创新中心）；Faculty of Education, The University of Hong Kong, Hong Kong SAR, China（香港大学教育学院）
Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
University of Science and Technology of China（中国科学技术大学）；Liupanshui Normal University（六盘水师范学院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；National University of Singapore（新加坡国立大学）
Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs
University of California（加州大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Massachusetts General Hospital（麻省总医院）；St. Jude Children’s Research Hospital（圣犹大儿童研究医院）；Brigham and Women’s Hospital & Harvard Medical School（布里法伦医院及哈佛医学院）
Harnessing Artificial Intelligence to Infer Novel Spatial Biomarkers for the Diagnosis of Eosinophilic Esophagitis
Technion IIT（以色列理工学院）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医学中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；Ann and Robert H. Lurie Children’s Hospital of Chicago（芝加哥安与罗伯特·H·卢里儿童医院）；Northwestern University（西北大学）
ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems
School of Computer Science；Shanghai Institute of AI Education East China Normal University Shanghai China；Technology East China Normal University Shanghai China；Shanghai Institute of AI Education East China Normal University；Technology East China Normal University
A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Anhui Province Key Laboratory of Science Education and Communication（安徽省科学教育与传播重点实验室）；iFLYTEK AI Research（讯飞AI研究院）
Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis
Shanghai Institute of AI Education（上海人工智能教育研究院）；School of Computer Science and Technology（计算机科学与技术学院）；East China Normal University（华东师范大学）；Department of Educational Psychology（教育心理学系）；Faculty of Education（教育学院）
MRN: Harnessing 2D Vision Foundation Models for Diagnosing Parkinson's Disease with Limited 3D MR Data
Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China（北京生物医学工程创新中心，生物科学与医学工程学院，北京航空航天大学，北京，中国）；Beijing Celebrain Technology Co., Ltd.（北京Celebrain技术有限公司）
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Unified Uncertainty Estimation for Cognitive Diagnosis Models
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China（中国科学技术大学安徽省大数据分析与应用重点实验室）；The University of Tennessee（田纳西大学）；iFLYTEK Research（讯飞研究院）；iFLYTEK AI Research (Central China)（讯飞人工智能研究（华中））
Breaking Robustness Barriers in Cognitive Diagnosis: A One-Shot Neural Architecture Search Perspective
School of Computer Science and Technology, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University（计算机科学与技术学院、教育部智能计算与信号处理重点实验室、安徽大学）；Anhui Province Key Laboratory of Intelligent Computing and Applications（安徽省智能计算与应用重点实验室）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院、安徽大学）；School of Artificial Intelligence, Anhui University（人工智能学院、安徽大学）；Institutes of Physical Science and Information Technology, Anhui University（物理科学与信息技术研究院、安徽大学）
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
University of Pennsylvania（宾夕法尼亚大学）；University of Electronic Science and Technology（电子科技大学）；Beijing Tongren Eye Center（北京同仁眼科中心）；Beijing Tongren Hospital（北京同仁医院）；Capital Medical University（首都医科大学）
