Abstract: In this talk, I will highlight a number of efforts focusing on transforming the education ecosystem through the use of data, recent advances in Artificial Intelligence (AI), and adaptive instructional systems (AISs). For instance, as part of the NSF-funded Learner Data Institute (LDI), we have been developing a science convergence framework to rigorously test the hypothesis that emerging learning ecologies that incorporate adaptive instructional systems (AISs) are capable of providing effective, engaging, equitable, efficient, and affordable individualized assistance for both learners and instructors, and that the parameters of these systems, e.g., effectiveness, can be improved over time given sufficient attention to evidence, captured as data, and expertise, provided by teams of interdisciplinary researchers. I will use a number of concrete examples to illustrate how expertise and theory, e.g., code comprehension theories, data, and AI can help provide better solutions to critical tasks such as assessment, the discovery of effective learning strategies, or domain modelling. Furthermore, I will present details about the design and development of a conversational intelligent tutoring system called iCODE which provides adaptive training of learners' code comprehension and learning processes. iCODE integrates Animated Pedagogical Agents (APAs), code comprehension theories, reading comprehension strategies (self-explanation, inferences), culturally responsive instructional design, and an Open Prosocial Learner Model (OPLM) to model, scaffold, and monitor reading comprehension strategies during code comprehension and learning tasks. The iCODE system can be used as a supplementary instructional tool in formal or informal, traditional or online education settings or as a standalone resource used by all learners (CS majors, non-majors, underrepresented groups) who want to improve their programming skills. I will conclude the talk with future plans and some conjectures and implications of our work and of latest AI developments such as ChatGPT for the digital transformation of education.

Short Bio: Dr. Vasile Rus is the Jack and Jane Professor of Computer Science and Intelligent Systems at The University of Memphis. He also serves as the Director of the Data Science Center and Program. Dr. Rus’ research interests are at the intersection of human, animal, and machine learning; specifically, he is exploring how to use Artificial Intelligence and the data revolution to further our understanding of how people learn, how to improve adaptive instructional systems (AISs), and how to make emerging learning ecologies that include online and blended learning with AISs more effective, efficient, engaging, equitable, relevant, and affordable. Dr. Rus’ research has been extensively funded by many federal funding agencies. Currently, he serves as PI on 4 projects funded by NSF and Department of Education and as co-PI on 2 projects, one funded by NSF and one by Department of Defense, for a total amount of funding of more than $11 million. Other accomplishments include 150+ peer-reviewed publications (conference papers, journal articles, book chapters), 3 best paper awards (5 best paper award nominations, all with his student advisees), winner of several research competitions (e.g., on automated Question Answering), and supervising and graduating 10 PhD students (3 females) and 37 Masters students (many from underrepresented groups).