Developing Hybrid Human-AI Regulation in Adaptive Learning Technologies to Support Young learners’ Self-regulated Learning
Adaptive Learning Technologies (ALTs) are widely used in classrooms in Dutch Primary Education. Students can practice different subjects with ALTs after teacher instruction. In ALTs task selection is adapted to student needs based on trace data. In this way ALTs take over (offload) regulation of learning of students. This is problematic, since it prevents students from learning how to regulate (monitor and control) their learning. Self-regulation skills are important to develop in students to enable them to come to deeper learning and for successful life-long learning. Hybrid Human-AI Regulation (HHAIR) can tackle this problem since it can offload regulation at the start and gradually onload regulation once a student has developed the necessary self-regulatory skills. Rianne works on a PhD-project that is part of Inge Molenaars’ ERC-funded project with the objective to design, develop and evaluate HHAIR to support development of young learners’ self-regulation skills in the context of ALTs.
Combining experience and interest in educational research, self-regulated learning and the use of technology in education, Rianne works one day a week as a teacher (design) research and thesis supervisor in a master program for teacher of all educational domains: Master program Designing Contemporary Learning (Master Ontwerpen van Eigentijds Leren, HAN University of Applied Sciences).