The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning

Hybrid systems combining artificial and human intelligence hold great promise for training human skills. In this paper, I position the concept of Hybrid Human-AI Regulation and illustrate this with an example of a first prototype of a Hybrid Human-AI Regulation (HHAIR) system. HHAIR supports self-regulated learning (SRL) in the context of adaptive learning technologies (ALTs) with the aim to develop learners' self-regulated learning skills. This prototype targets young learners (10–14 years) for whom SRL skills are critical in today's society. Many of these learners use ALTs to learn mathematics and languages every day in school. ALTs optimize learning based on learners' performance data, but even the most sophisticated ALTs fail to support SRL. In fact, most ALTs take over (offload) regulation from learners. In contrast, HHAIR positions hybrid regulation as a collaborative task of the learner and the AI which is gradually transferred from AI-regulation to self-regulation. Learners will increasingly regulate their own learning progressing through different degrees of hybrid regulation. In this way HHAIR supports optimized learning and the transfer and development of SRL skills for lifelong learning (future learning). The HHAIR concept is novel in proposing a hybrid intelligence approach training human SRL skills with AI. This paper outlines theoretical foundations from SRL theory, hybrid intelligence and learning analytics. A first prototype in the context of ALTs for young learners is described as an example of hybrid human-AI regulation and future advancement is discussed. In this way, foundational theoretical, empirical, and design work are combined in articulating the concept of Hybrid Human-AI Regulation which features forward adaptive support for SRL and transfer of control between human and AI over regulation.