Large language models (LLMs) have shown the potential for generating educational content at scale, assisting educators in creating practice tasks or synthesizing data for training educational models. However, LLMs suffer from the ``Artificial Hivemind'' effect, where they produce homogeneous content. This homogeneity limits the diversity of LLM-generated tasks, a crucial factor in these educational settings. In this paper, we investigate how to increase the diversity of generated tasks while keeping their utility high. Inspired by the divergent--convergent thinking stages in creativity literature, we propose a prompting framework with two reasoning stages: (1) exploring the creative space, and (2) satisfying the input requirements. We evaluate CreativeDC, a method instantiated from this framework in the domain of Python programming, using both automated metrics and expert evaluation. Results show that CreativeDC produces significantly more distinct high-utility tasks (about $1.6\times$) than baselines. Our work offers an effective approach for generating and evaluating more diverse tasks at scale.
Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
Department of Mathematics and Computer Science, University of Catania, Italy（卡塔尼亚大学数学与计算机科学系）；Institute of Cognitive Sciences and Technologies (ISTC), National Research Council of Italy (CNR)（意大利国家研究委员会认知科学与技术研究所（ISTC））
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University（国家教育大数据工程研究中心，教育人工智能学院，中央财经大学）
Generating Reading Comprehension Exercises with Large Language Models for Educational Applications
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China（光学电子与计算机工程学院，上海理工大学，上海，中国）；Chongqing Academy of Science and Technology, Chongqing, China（重庆市科学技术院，重庆，中国）
Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation
Aarhus University（奥胡斯大学）；University of Copenhagen（哥本哈根大学）；Dept. of Computer Science and Artificial Intelligence, University of Granada（格拉纳达大学计算机科学与人工智能系）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
Computer Engineering Department, Sharif University of Technology, Iran（谢尔盖大学计算机工程系，伊朗）；Qatar Computing Research Institute, Qatar（卡塔尔计算研究所，卡塔尔）；Computer Engineering Department, University of Isfahan, Iran（伊斯法罕大学计算机工程系，伊朗）；Independent Researcher（独立研究者）
LLM Agents for Education: Advances and Applications
Squirrel Ai Learning；Fudan University（复旦大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications
Dainam University（大南大学）；VNU University of Engineering and Technology（越南国家大学工程技术大学）；Latrobe University（拉筹伯大学）；University of South Eastern Norway（东南挪威大学）
Since its introduction in 2011, there have been over 4000 MOOCs on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to democratize knowledge dissemination and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience. To address the challenges, in this thesis, we propose a frame…
EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation
East China Normal University（东华大学）；Shanghai Institute of Al for Education（上海人工智能教育研究院）；The Chinese University of Hong Kong, Shenzhen School of Data Science（香港中文大学（深圳）数据科学学院）
Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation
Learning, Data and Robotics (LDR) ESIEA Lab（学习、数据与机器人（LDR）ESIEA实验室）；Department of Computer Engineering（计算机工程系）；Graduate School of Management and Innovation（管理与创新研究生院）；Faculty of Science（科学学院）；Lebanese University（黎巴嫩大学）
Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models
Concordia University（康科迪亚大学）；Concordia Institute for Information Systems Engineering（康科迪亚信息系统工程研究所）；Toronto Metropolitan University（多伦多 Metropolitan 大学）
Enhancing Educational Efficiency: Generative AI Chatbots and DevOps in Education 4.0
State University of Novi Pazar（新帕扎尔国立大学）；University of Criminal Investigation and Police Studies（刑事侦查与警察研究大学）；Higher Technical Professional School in Zvečan（兹韦钱高等技术专业学院）；Academy Of Technical And Art Applied Studies Belgrade（贝尔格莱德技术与艺术应用研究学院）
Virtual Reality (VR) is increasingly being recognized for its educational potential and as an effective way to convey new knowledge to people, it supports interactive and collaborative activities. Affordable VR powered by mobile technologies is opening a new world of opportunities that can transform the ways in which we learn and engage with others. This paper reports our study regarding the application of VR in stimulating interdisciplinary communication. It investigates the promises of VR in interdisciplinary education and research. The main contributions of this study are (i) literature rev…
Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
Department of Computer Science Engineering, Universidad Autónoma de Madrid（马德里自治大学计算机科学工程系）；Digital Futures Institute, Teachers College Columbia University（哥伦比亚大学教师学院数字未来研究所）
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Information Technology & Management, University of International Business and Economics（对外经济贸易大学信息技术与管理学院）
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation
Department of Computer Science and Information Engineering, National Cheng Kung University（计算机科学与信息工程系，国立成功大学）；Department of Biomedical Informatics, Harvard Medical School, Harvard University（生物医学信息学系，哈佛医学院，哈佛大学）
