Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.
From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation
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）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究所）；iFLYTEK Research（iFLYTEK研究院）
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Enuma Inc（Enuma公司）；University of Memphis（孟菲斯大学）；Daegu National University of Education（大邱教育大学）；Seoul National University（首尔大学）；Seoul Metropolitan Office of Education（首尔市教育厅）；Korea University（高丽大学）
The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Xunfei Healthcare Technology Co., Ltd（讯飞医疗科技有限公司）；School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；Guangzhou University（广州大学）
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）
Comparison of Large Language Models for Generating Contextually Relevant Questions
Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电气工程学院）；Data-Driven Innovation Initiative, Kyushu University（九州大学数据驱动创新倡议）
A Report on the llms evaluating the high school questions
School of Environmental Science and Engineering, Guangdong University of Technology（广东工业大学环境科学与工程学院）；School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
IBM Research Europe（IBM欧洲研究院）；IBM Research（IBM研究院）；WeChat AI（微信AI）；The Hong Kong University of Science and Technology（香港科技大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Nankai University（南开大学）
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Emory University（埃默里大学）；Northeastern University（东北大学）；UMass Chan Medical School（马萨诸塞大学陈医学院）；University of Minnesota（明尼苏达大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos
TIB – Leibniz Information Centre for Science and Technology（蒂宾根-莱比锡信息科学与技术研究中心）；Hochschule Hannover – Data（汉诺威高等学院-数据）；H Institute for Applied Data Science（汉诺威应用数据科学研究所）；L3S Research Center – Leibniz University Hannover（L3S研究中心-汉诺威莱比锡大学）
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Rensselaer Polytechnic Institute（伦斯勒理工学院）；IBM Research（IBM研究院）；University of Washington（华盛顿大学）；University of Notre Dame（圣母大学）；WeChat AI, Tencent（腾讯微信人工智能）；University of California Irvine（加州大学欧文分校）
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages
Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering of the University of Porto (FEUP)（人工智能与计算机科学实验室（LIACC）工程学院（FEUP））
Evaluating Multimodal Large Language Models on Educational Textbook Question Answering
Faculty of Computing and Information Technology（计算与信息科技学院）；King Abdulaziz University（阿卜杜勒阿齐兹大学）；FAST School of Computing（快速计算学院）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；School of Computing（计算学院）；Macquarie University（麦考瑞大学）；Center of Research Excellence in AI and Data Science（人工智能与数据科学卓越研究中心）
非洲的学生与教师的比例很高，限制了学生获得教师的机会。因此，学生们很难得到他们问题的答案。在这项工作中，我们扩展了我们以前的人工智能教学助手Kwame，将其调整为科学教育，并将其部署为Web应用程序。Kwame for Science基于西非高中证书考试（WASSCE）的综合科学科目回答学生的问题。Kwame for Science是一个基于句子BERT的问答网络应用程序，它显示3个段落作为答案，并在回答科学问题时显示信心分数。此外，它显示了前5个相关的过去考试的问题和他们的答案，除了3段。我们对Kwame for Science进行了为期2.5周的实际部署的初步评估，结果显示前3名的准确率为87.5%（n=56），共有11个国家的190名用户。Kwame for Science将为非洲数百万人提供可扩展的、具有成本效益的高质量远程教育。
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China（计算机科学与工程系，香港科技大学，香港特别行政区，中国）；Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan（机械工程系，东京大学，东京，日本）
