2022-02-01 15:30 - 17:00 Online

Hybrid systems combining artificial and human intelligence hold great promise for training human skills. In this keynote I position the concept of Hybrid Human-AI Regulation and conceptualize an example of a Hybrid Human-AI Regulation (HHAIR) regulation system to develop learners’ Self-Regulated Learning (SRL) skills within Adaptive Learning Technologies (ALTs).

This example of HHAIR 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) control and monitoring from learners. HHAIR, on the other hand, aims to gradually transfer regulation of learning 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 transfer (deep learning) and development of SRL skills for lifelong learning (future learning).

This concept is innovative in proposing the first hybrid systems to train human SRL skills with AI. The design of HHAIR aims to contribute to four scientific challenges: i) identify individual learner’s SRL during learning; ii) design degrees of hybrid regulation; iii) confirm effects of HHAIR on deep learning; and iv) validate effects of HHAIR on SRL skills for future learning. The talk outlines how to develop advanced measurement of SRL and algorithms to drive hybrid regulation for developing SRL skills in ALTs. The concept of Hybrid Human-AI Regulation has potential to support the development of SRL and specifically this HHAIR example outlines a reflection those principles.

You are welcome to join this meeting. Send an e-mail to for the ZOOM link.