With the rapid development of information technology, adaptive learning has become a crucial aspect in the field of education, offering efficient, effective, and customized learning paths to engage students. However, the existing adaptive learning systems lack a comprehensive digital portrait of students, hindering the ability to identify students’ Zone of Proximal Development. Fortunately, learning analytics community has developed a quite numerous techniques to collect various types of data about learners, which can be used to create learner profiles. These profiles have been leveraged to better support learning, including personalized recommendations for readings and activities linked to learning design.

When building a comprehensive learner profile, it is essential to consider not only the knowledge acquisition but also the competencies and skills of learners in line with the 21st-century skills theory. Although some studies have used learning analytics techniques to collect learners' behavioral data and explore their competencies or skills, such as critical thinking skills, there is still a gap between the research findings and the learner profiles built by existing adaptive learning systems. It has been noted that existing adaptive learning systems do not adequately utilize existing research results to create learner profiles or comprehensively synthesize learning analytics data to build such profiles, focusing mainly on students' mastery of knowledge.

I will be presenting the report on our adaptive learning project, which we have been developing for several years. The novelty of this system, is that we are putting the studies of learning analytics community, to build a full-scale learner profile, as the learner model in our adaptive learning system. In summary, the presentation will focus on building a full-scale learner profile and an AI adaptive learning system.

Dr. Xiaoqing Gu is a professor and head of Department of Educational Information Technology, Faculty of Education, East China Normal University, China. She is the head of Shanghai Engineering Research Center of Digital Education Equipment, and now leads the AIED research community with scholars and practitioners. Professor Gu has long been engaged in the research and practice of educational informationization. Her research interests include the areas of learning science and learning technology, computer-supported collaborative learning (CSCL), learning analytics and learner profiling, ICT-integrated pedagogical innovation in which she has published more than 150 publications in internationally journals and conferences, more than 10 books and teaching materials. Professor Gu is the chief editor of IJSmartTL (International Journal of Smart Technology and Learning), board member of Journal of Computer Assisted Learning, Education Technology Research and Development, The Internet and Higher Education, Journal of Computer in Education, International Journal of Computer-Supported Collaborative Learning, and Journal of East China Normal University (Educational Science).