Self-Regulated Learning (SRL) with AI in Problem-Based Learning

The development of Artificial Intelligence (AI) technology presents both new opportunities and challenges for educators when teaching. Leveraging AI with its anthropomorphic features and interactive capabilities has the potential to create improved digital environments that support self-regulated learning (SRL), especially during problem-based learning (PBL). In this position paper, we focus on developing a framework for embedding AI and SRL within a PBL context, through the use of an Interactive Virtual Agent (IVA). Modern IVA technologies frequently offer natural interactions between a virtual agent (e.g., tutors, instructors) and a student in which a multimodal interface is used, including recognition and synthesis of affect and attitude expressed in speech tonality (content and voice), facial expression, gaze, gestures, and body language. Such systems, however, often miss important functionality that is desirable for educational needs such as SRL skills, providing teacher support in the classroom, and human-level social compatibility. These gaps could be addressed by integrating key SRL processes (e.g., goal-setting, self-monitoring, self-evaluating) into Biologically Inspired Cognitive Architecture (eBICA), and further combining the result with a Virtual Agent visualization, in the form of a Virtual Tutor, based on the game engine UE5. This paper will begin with an overview of PBL centered within SRL theory, followed by an introduction to how AI technology, and finally how it can be leveraged during PBL and teaching. Potential implications for practice of this SRL-centered framework will be discussed.