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HHAIR

financed

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 Adap... see more

31/01/2026

STICHTING RADBOUD...

1M€

Project Budget: 1M€

Project leader

STICHTING RADBOUD UNIVERSITEIT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2021-01-19

ERC-2020-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada does 5 years

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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2 Participantes

STICHTING RADBOUD UNIVERSITE...

1.50M€ | Leader

ENIGMEDIA

176.25K€ | participant

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Project duration: 60 months Date Start: 2021-01-19
End date: 2026-01-31

Project leader

STICHTING RADBOUD UNIVERSITEIT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 1M€

participation deadline Sin fecha límite de participación.

Project description 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 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 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 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 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.

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