Hybrid Human-AI Regulation: Supporting Young Learners' Self-Regulated Learning

Hybrid systems combining artificial and human intelligence hold great promise for training human skills.

I propose to develop Hybrid Human-AI Regulation (HHAIR) to develop learners Self-Regulated Learning (SRL) skills within Adaptive Learning Technologies (ALTs). HHAIR targets young learners (10-14-years) for whom SRL skills are critical in todays 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 project is ground-breaking in developing the first hybrid systems to train human SRL skills with AI.The design of HHAIR resolves four scientific challenges: i) identify individual learners 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 four design challenges are addressed by investigating ALTs trace data in exploratory studies (WP1), applying these insights to develop HHAIR in design studies (WP2), investigating immediate effects on deep learning in short-term field studies (WP3) and effects on SRL-skills for future learning in long-term field studies (WP4).

The AI@EDU infrastructure will connect HHAIR to ALTs used daily in schools across Europe.

The project will develop advanced measurement of SRL and algorithms to drive hybrid regulation for developing SRL skills in ALTs.