With the popularity of online/virtual learning, especially during the period of epidemic prevention of the COVID-19, Artificial Intelligence (AI) technologies have shown their good potential to advance education by solving various critical and complex educational issues with promising results. Data in education may originate from a variety of learning contexts, such as interactive learning environments, educational games, learning and information management systems, intelligent tutoring systems, and data-rich learning activities, etc. AI technologies bring new opportunities for developing data-driven models/algorithms/tools for dealing with various tasks in education, including prediction, classification, reasoning and recommendation, etc. While recent years have witnessed growing efforts and big progresses in the AI domain, developing and applying AI technologies for the educational practice (for the purpose of deeply understanding human learning) still faces some challenges, including the uncertainty, security and privacy issues of educational data, the gap between educational objectives and the use of non-interpretable/blackbox models, high complexity in the interactive and iterative process of human learning, etc. Therefore, new insights on how to introduce AI in education, new advances of AI-based educational technologies and applications, are expected to further pave the way of “AI+Education”. This session aims to provide a forum for both the academic and industrial communities to report their research progress on applying AI to education and discuss recent advances of handling challenges encountered in AI educational technologies and practices. Topics of interest include (in no particular order) but are not limited to following:
• AI-based Multimodal Analytics for Understanding Human Learning
• Interactive/Self-adaptive/Personalized Learning
• Knowledge Representation and Reasoning, Knowledge Tracing
• Dropout Prediction, Student Performance Prediction
• Intelligent Learning/Tutoring/Monitoring systems
• Motivational Diagnosis and Feedback
• AI-based Cognitive Diagnosis
• Adaptive Question-Answering and Dialogue
• Automated Grading of Assignments, Automated Essay Scoring
• AI-based Tools for Formative and Summative Assessment
• Automated feedback and recommendations
• Learning Analytics
• Educational Data Mining
• Data Analytics & Big Data in Education
• Educational Games and Robotics
• Public Education Datasets, Educational Data Mining Packages/Platforms
• Immersive Learning and Multimedia Applications
• AI-Driven Wearable Devices and Interfaces in E-learning
• Interpretable AI Theory and Technologies for Education
• AI-based VR or AR in Education
• Security and Privacy in Education
Changqin Huang, Zhejiang Normal University, China.
Changqin Huang received his Ph.D. degree from the Department of Computer Science and Technology at Zhejiang University, China in 2005. Currently, he is an Outstanding Professor and Director of the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, at Zhejiang Normal University, China. He was a Visiting Scientist at Zhejiang University, a Visiting Professor at La Trobe University, and a Visiting Professor at University of California, Irvine. He is a regular reviewer for top journals including IEEE TNNLS, IEEE TCYB, IEEE TKDE, Neural Networks, Information Sciences, Neurocomputing. His research interests include service computing, big data, and semantic information retrieval. He is an awardee of "Pearl River Scholar". Dr. Huang is an Associate Editor of IEEE Transactions on Learning Technologies, a senior member of the China Computer Federation (CCF), a member of ACM and IEEE.
Ming Li, Zhejiang Normal University, China.
Ming Li received his PhD degree from the Department of Computer Science and IT at La Trobe University, Australia. He is currently a “Shuang Long Scholar” Distinguished Professor with the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, China. He has published in top-tier journals and conferences, including IEEE TCYB (one paper is ranked as ESI Highly Cited Paper), ACM TMOS, IEEE TII, Neural Networks, Information Sciences, NeurIPS, ICML. He is a member of IEEE, a member of China Computer Federation (CCF), a member of Chinese Association for Artificial Intelligence (CAAI), and an accredited member of Australian Mathematical Society (AustMS). He is a regular reviewer for top journals including IEEE TNNLS, IEEE TCYB, IEEE TKDE, Neural Networks, Information Sciences, Neurocomputing. His research interests include machine learning, neural networks for graphs, graph learning representation, randomized learning algorithms, educational data analytics, and approximation theory. He, as a leading guest editor, is currently organizing a special issue, i.e., “Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications”, in IEEE TNNLS.
Simone Scardapane, Sapienza University of Rome
Simone Scardapane received his B.Sc. in Computer Engineering at Roma Tre University in 2009, and a M.Sc. in Artificial Intelligence and Robotics in Sapienza University two years later. After working one year as a software/web developer, he obtained a Ph.D. in the same university in 2016, researching mainly in the fields of distributed machine learning and adaptive audio processing. Currently, he is an assistant professor at Sapienza University working in the fields of graph, audio, and image deep learning. He has published more than 80 papers in international journals and conferences, and he is a member of various committees including the IEEE CIS Social Media Sub-Committee, the IEEE Task Force on Reservoir Computing, and the “Machine learning in geodesy” joint study group of the International Association of Geodesy. He is chair of the Statistical Pattern Recognition Techniques TC of the International Association for Pattern Recognition and chairman and co-founder of the Italian Association for Machine Learning.