W. Lewis Johnson, Ph.D：
Dr. Johnson is an internationally recognized expert in AI education. For his work on the first Alelo immersive game, Tactical Iraqi, he won DARPA’s Significant Technical Achievement Award. Alelo’s Enskill English is a finalist for the British Council’s award for Digital Innovation in English Language Teaching. He has been a past President of the International AI in Education Society and was co-winner of the 2017 Autonomous Agents Influential Paper Award for his work in the field of pedagogical agents. He has been invited to speak at many international conferences such as the International Conference on Intelligent Tutoring Systems, and presented a Distinguished Lecture at the National Science Foundation.
Dr. Johnson received his undergraduate degree in Linguistics from Princeton University and his Ph.D. in Computer Science from Yale University.
Talk Title：The Seven Roles that AI Can Play to Transform Education
Abstract: The goal of artificial intelligence (AI) in education should be not to replace teachers but to work alongside them, to assist both learning and teaching. AI can perform tasks that are time consuming and difficult for teachers to perform on their own. We can identify at least seven roles that AI can play in the learning process: Communicate, Assess, Critique, Guide, Orchestrate, Summarize, and Construct. Advances in natural language processing and machine learning, and the availability of learner data increasingly makes all of these roles possible. As AI combines these roles it sets the stage for a fundamental transformation of learning methods and learning organizations. These seven roles are being realized in Alelo Enskill, a cloud-based platform for learning communication skills. Students and teachers in over twenty countries are learning English with Enskill. It is a finalist for the British Council’s award for Digital Innovation in English Language Teaching.
Talk Title: An introduction to General Intelligent Framework for Tutoring (GIFT)
bio: Dr. Min Chi is an Assistant Professor in the Department of Computer Science at North Carolina State University. She is a nationally and internationally recognized expert in the field of data-driven pedagogical policy induction for Intelligent Tutoring Systems. Dr. Chi’s research expertise and impact has been recognized through numerous awards including an NSF CAREER Award, a prestigious Alcoa Foundation Engineering Research Achievement Award, five Best Paper, Best Student Paper, and Outstanding Paper Awards. Her international visibility is manifested via a prestigious Executive Committee membership in the International Artificial Intelligence in Education Society since 2017 and the Program Co-Chair for the 9th International Conference on Educational Data Mining in 2016.
Talk Title: The Past, the Present and the Future of Intelligent Tutoring Systems
Abstract: Technologies to support learning and education, such as Intelligent Tutoring Systems, have achieved great success. Today, Intelligent Tutoring Systems are in widespread use in grade-middle-high schools and colleges and are enhancing the student learning experience. In recent years, a range of types of interactive educational technologies have also become prominent and widely used, including homework support and tutoring systems, science simulations and virtual labs, educational games, on-line resources, massively open online courses, and highly interactive web-based courses. Some have experimentally established learning benefits. These systems are increasingly being instrumented to collect vast amounts of "Big Data" and more and more of it is freely available. Such data can be used to help advance our understanding of student learning and create better, more intelligent, interactive, engaging, and effective education. The creation of intelligent learning technologies from data has significant potential to transform the traditional educational system by providing a low-cost way to adapt learning environments to individual students’ needs and by informing advanced research on human learning. This research creates the technology for a new generation of data-driven Intelligent Tutoring Systems, enabling the rapid creation of individualized instruction that supports learning in science, technology, engineering, and mathematics (STEM). The net result of this work is a modular framework of educational data mining methods that offer student adaptive, individualized support at multiple granularities, that have been implemented, iteratively refined, and empirically validated for learning impact and robustness across systems and three STEM domains including logic, probability, and programming, where building traditional Intelligent Tutoring Systems is extremely challenging.