Artificial Intelligence (AI) has dramatically transformed a variety of domains. However, education, a crucial component of our society still remains a relatively under-explored domain. In fact, the increasingly digitalized education tools and the popularity of the massive open online courses have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education. Recent years have witnessed growing efforts from AI research community devoted to advancing our education. Although it is still in the early stage, promising results have been achieved in solving various critical problems in education. For example, knowledge tracing, which is a intrinsically difficult problem due to the complexity under human learning procedure, has been solved successfully with powerful deep neural networks that can fully take the advantages of massive student exercise data. Besides the achievement in improving the student learning efficiency, similar excitement has been generated in other areas of education. For instance, researchers have also devoted to reducing the monotonous and tedious grading workloads of teaching professionals by building automatic grading systems that are underpinned by effective models from natural language process fields. Despite aforementioned success, developing and applying AI technologies to education is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues. Therefore, it is timely and necessary to provide a venue, which can bring together academia researchers and education practitioners (1) to discuss the principles, limitations and applications of AI for education; and (2) to foster research on innovative algorithms, novel techniques, and new applications to education.
CALL FOR PAPER
We invite the submission of novel research paper (6 pages plus references), demo paper (4 pages plus references), visionary papers (4 pages plus references) as well as extended abstracts (2 pages plus references). Submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. All papers will be peer reviewed, single-blinded. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. All the papers are required to be submitted via EasyChair system. For more questions about the workshop and submissions, please send email to firstname.lastname@example.org.
We encourage submissions on a broad range of AI technologies for various education domains. Topics of interest include but are not limited to the following:
- - Emerging Technologies in education
- - Evaluation of education technologies
- - Immersive learning and multimedia applications
- - Implications of big data in education
- - Self-adaptive learning
- - Individual and personalized education
- - Intelligent Learning Systems
- - Intelligent Tutoring and Monitoring Systems
- - Automatic assessment in education
- - Automated grading of assignments
- - Learning technology for Lifelong Learning
- - Course development techniquesn
- - Mining and web mining in Education
- - Learning tools experiences and Cases of Study
- - Life long education
- - MOOC’s and Data Analytics
- - Social media in education
- - Smart education
- - Education Analytic approaches, methods, and tools
- - Knowledge management for learning
- - Learning analytics and educational Data Mining
- - Smart classroom
- - Dropout prediction
- - Knowledge tracing
- - Tracking learning activities
- - Uses of multimedia for education
- - Wearable computing technology In e-learning
- - Analysis of communities of learning
- - Computer-aided assessment
- - Course development techniques
- - Automated feedback and recommendations
- - Big data analytics for education
December 04, 2019: Workshop paper submission due (23:59, Pacific Standard Time)
December 15, 2019: Workshop paper notifications
January 15, 2020: Camera-ready deadline for workshop papers
February 8, 2020: Workshop Date
AI Singapore: AI in Education Grand Challenge
Dr. Bryan Low
Dr. Su Su Ma
- Bio: Bryan Low is the deputy director of research and technology, AI Singapore. He is also an assistant professor of School of Computing, National University of Singapore. Su Su Ma is the head of research management, AI Singapore.
- Abstract: AI Singapore (AISG - https://www.aisingapore.org/) is a national AI programme launched by the National Research Foundation (NRF) to anchor deep national capabilities in Artificial Intelligence (AI) thereby creating social and economic impacts, grow the local talent, build an AI ecosystem, and put Singapore on the world map. The programme office is hosted by the National University of Singapore (NUS) and brings together all Singapore-based research institutions and the vibrant ecosystem of AI start-ups and companies developing AI products to perform use-inspired research, grow the knowledge, create the tools, and develop the talent to power Singapore's AI efforts. AI Singapore AI in Education Grand Challenge is a competitive research funding initiative aimed at encouraging and supporting new ideas that adopt AI technologies and innovations to explore how AI could potentially be applied to transform or revolutionize the way we educate the next generation of students. Following the previous AI in Health Grand Challenge, AISG has partnered the Ministry of Education (MOE) in launching the next AI in Education (AIEd) Grand Challenge.
AI the Next Step for Education: Tech Innovations Making Our Classrooms Smarter
Dr. Zitao Liu
- Bio: Zitao Liu is the Head of AI Lab at TAL Education Group (NYSE:TAL), one of the largest leading education and technology enterprises in China. He studies and develops AI approaches to tackle some of the hard-core problems in AIED, such as automatic short answer grading, knowledge tracing, etc. He has published in highly ranked conference proceedings, such as AAAI, AIED, etc. and serves as top tier AI conference/workshop organizers/program committees. Before joining TAL, Zitao was a senior research scientist at Pinterest and received his Ph.D degree in Computer Science from University of Pittsburgh.
- Abstract: With the recent development of AI, there has been tremendous changes in both offline and online education. Entire in-class interactions and behaviors between students and instructors have been structured and stored, which provide valuable information for analyzing class performance and improving the learning experience. In this talk, I will first show some successful applications we deployed in TAL's offline and online classrooms. Then I will outline the challenges we meet during the course of building real-world AI+Edu applications. After that, I will talk about the three initiatives we developed on (1) building a quality-assured online learning platform, which is able to utilize multimodal information from the online environment to conduct pedagogical monitoring and alerting; (2) studying a cost-effective and consistent approach of verbal fluency evaluation, which help elementary students improve their oral language skills after school; and (3) getting inside the black box of classroom by neural multimodal learning, which helps teachers get instant feedback on their pedagogical instructions.
Algorithmic Openness in Data Intensive Education Analytics: K-12 Early Warning Systems, Prediction, Accuracy, and Visual Data Analytics
Dr. Alex Bowers
- Bio: Alex J. Bowers is an Associate Professor of Education Leadership at Teachers College, Columbia University, where he works to help school leaders use the data that they already collect in schools in more effective ways to help direct the limited resources of schools and districts to specific student needs. His research focuses on the intersection of effective school and district leadership, organization and HR, data driven decision making, student grades and test scores, student persistence and dropouts. His work also considers the influence of school finance, facilities, and technology on student achievement. He studies these areas through the application of Education Leadership Data Analytics (ELDA), which is at the intersection of education leadership, evidence-based improvement cycles, and data science.
FACT: An Automated Teaching Assistant
Dr. Kurt VanLehn
- Bio: Kurt VanLehn is the Diane and Gary Tooker Chair for Effective Education in Science, Technology, Engineering and Math at Arizona State University. He is also a Professor of Computer Science. He received a Ph. D. from MIT in 1983 in Computer Science, and worked at BBN, Xerox PARC, CMU and the LRDC (University of Pittsburgh). He founded and co-directed two large NSF research centers (Circle; the Pittsburgh Science of Learning Center). He has published over 185 peer-reviewed publications, is a fellow in the Cognitive Science Society, and is on the editorial boards of Cognition and Instruction and the International Journal of Artificial Intelligence in Education. Dr. VanLehn has been working in the field of intelligent tutoring systems since such systems were first invented. Most of his current work seeks new applications of this well-established technology. Four current projects are: (1) FACT, a classroom orchestration system for helping middle school math teachers both deeply analyze student work and manage the flow of ideas and student work across individual, group and whole-class activities; (2) TopoMath, an intelligent tutoring system that teaches high school and developmental math students how to solve algebra word/modeling problems by displaying their structural topology; (3) Dragoon, an intelligent tutoring system that imparts skill in constructing models of dynamic systems so rapidly that it has been used in high school science classes, university sustainability classes and Navy electronics classes to help students understand the systems more deeply; and (4) SEATR, a general-purpose adaptive task selection service that is currently being used in an intelligent tutoring system for organic chemistry.
Teachers in Social Media: Applications in Computational Education Science
Dr. Kaitlin Torphy
- Bio: Kaitlin Torphy, Ph.D. is the Lead Researcher and Founder of the Teachers in Social Media Project at Michigan State University. This project considers the intersection of cloud to class, nature of resources within virtual resource pools, and implications for equity as educational spaces grow increasingly connected. Dr. Torphy conceptualizes the emergence of a teacherpreneurial guild in which teachers turn to one another for instructional content and resources. She has expertise in teachers’ engagement across virtual platforms, teachers’ physical and virtual social networks, and education policy reform. Dr. Torphy was a co-PI and presenter for an American Education Research Association conference convened in October 2018 at Michigan State University on social media and education. She has published work on charter school impacts, curricular reform, teachers’ social networks, and presented work regarding teachers’ engagement within social media at the national and international level. Her other work examines diffusion of sustainable practices across social networks within The Nature Conservancy. Dr. Torphy earned a Ph.D. in education policy, a specialization in the economics of education from Michigan State University in 2014 and is a Teach for America alumni and former Chicago Public Schools teacher.
To Be Decided
Dr. Vincent Aleven
- Bio: Dr. Vincent Aleven is a Professor of Human-Computer Interaction at Carnegie Mellon University in Pittsburgh, USA. He has over 25 years of experience in research and development of adaptive learning technologies, based on cognitive theory and self-regulated learning theory. He has investigated widely how such technologies can be most effective, with projects ranging from computer-based tutoring of help seeking, to a website with intelligent tutoring software for middle-school mathematics, to a real-time mixed-reality teacher awareness tool. He and his colleagues have also created easy-to-use, easy-to-learn authoring tools for adaptive learning technologies. He has over 250 publications to his name. He is co-editor-in-chief of the International Journal of Artificial Intelligence in Education. He also was co-editor of the International Handbook on Metacognition in Computer-based Learning Environments. He and his colleagues and students have won 10 best paper awards at international conferences. He is or has been PI on 12 major research grants and co-PI on 11 others.
AI in Education
Dr. Salil Mehta
- Bio: Salil has over two decades of leadership experience in various industries, including the White House, and in AI and education. He has also been teaching data science at Columbia University, and Georgetown University. He is the creator of the Salil Statistics (statisticalideas.blogspot.com) website that has over ¼ million followers. Salil is currently a statistics director at ETS, overseeing innovative business applications for about a hundred data statisticians. His various experience includes regulating and defending audits of information models, at the federal government.