Foundations for Human-AI teaming for self-regulated learning with explainable AI (XAI)

Abstract

This discussion takes a human-centred perspective of the contributions of the collection. Its papers explore diverse, new uses of AI with rich, multimedia sensor data towards new ways to measure and understand self-regulated learning. This work can contribute to the learning sciences. It can also provide a foundation for future personalised teaching and learning systems with explainable AI (XAI) and learner control. I will discuss the papers from that perspective with a focus on an important form of XAI in education – the Open Learner Models (OLM). When suitably designed, OLMs can empower a learner to: (1) contribute data about themself and their self-regulated learning processes, complementing conventional and multimedia data; (2) scrutinise and control learner data collection and use in AI-based systems and (3) be the controlling partner in AI-teaming that scaffolds their self-regulated learning processes.