Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge
Rice University（莱斯大学）；University of Cambridge（剑桥大学）；Eedi；Microsoft Research（微软研究院）；Alan Turing Institute（艾伦·图灵研究所）
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, S… 展开作者
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
University of Auckland（奥克兰大学）；Microsoft（微软公司）；Worcester Polytechnic Institute（伍斯特理工学院）；EPFL（洛桑联邦理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Carleton College（卡尔顿学院）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-assisted Instruction
College of Information Science, University of Arizona（亚利桑那大学信息科学学院）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）；Department of Systems and Industrial Engineering, University of Arizona（亚利桑那大学系统与工业工程系）；Department of Industrial Engineering, University of Sonora（索诺拉大学工业工程系）；Leonhard Center for Enhancement of Engineering Education, The Pennsylvania State University（宾夕法尼亚州立大学莱昂哈德工程教育提升中心）
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
ByWater Institute, Tulane University（杜兰大学拜沃特研究所）；Office of Teaching, Learning, and Technology, University of Iowa（爱荷华大学教学、学习与技术办公室）；River-Coastal Science and Engineering, Tulane University（杜兰大学河流与海岸科学与工程系）
本研究评估了在R1公立大学工程本科课程中应用的AI学习框架Educational AI Hub，通过混合方法发现学生认可其便利性但存在伦理顾虑，强调需提升可用性、伦理透明度并辅以教师指导以促进有效AI学习参与。
