Dr. Xiangen Hu is a professor in the Department of Psychology, Department of Electrical and Computer Engineering and Computer Science Department at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and is professor and Dean of the School of Psychology at Central China Normal University (CCNU). Dr. Hu received his MS in applied mathematics from Huazhong University of Science and Technology, MA in social sciences and Ph.D. in Cognitive Sciences from the University of California, Irvine. Dr. Hu is the Director of Advanced Distributed Learning (ADL) Partnership Laboratory at the UofM, and is a senior researcher in the Chinese Ministry of Education’s Key Laboratory of Adolescent Cyberpsychology and Behavior.

Dr. Hu's primary research areas include Mathematical Psychology, Research Design and Statistics, and Cognitive Psychology. More specific research interests include General Processing Tree (GPT) models, categorical data analysis, knowledge representation, computerized tutoring, and advanced distributed learning. Dr. Hu has received funding for the above research from the US National Science Foundation (NSF), US Institute of Education Sciences (IES), ADL of the US Department of Defense (DoD), US Army Medical Research Acquisition Activity (USAMRAA), US Army Research Laboratories (ARL), US Office of Naval Research (ONR), UofM, and CCNU.

This talk presents a minimum set of requirements for the improvable components of AIS, where the components are the human Learners, the Instructional Process, instructional Environments, and the physical, digital and human resources necessary for effective and efficient AIS. We will only present the set of requirements at the conceptual level. Specifically, we will consider improvable requirements, the measure of improvability, self-improvable requirements, and measure of self-improbability for each of the four components. We will focus on the self-improvable requirements, and measure of self-improbability of digital resources for AIS. There are existing such resources (we call them dynamic learning resources) in the form of Intelligent Tutoring Systems (ITS) and other AI-enabled learning resources such as specially designed serious games and virtual training environments. In this paper, we attempt to present the requirements for improvable AIS components in a research framework where human learners and dynamic learning resources play symmetrical roles. Such symmetry research framework made it reasonable to consider self-improvable requirements, and measure of self-improbability of the dynamic learning resources similar to their human counterparts.