Human and artificial intelligence collaboration for socially shared regulation in learning

Abstract

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human‐AI collaboration by introducing a novel trigger concept and a hybrid human‐AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human‐AI collaboration in facilitating effective SSRL in teaching and learning. Practitioner notesWhat is already known about this topic For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor. Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning. It is questionable whether traditional theories of how people learn are useful in the age of AI. What this paper adds Introduces a trigger concept and a hybrid Human‐AI Shared Regulation in Learning (HASRL) model to offer insights into how the human‐AI collaboration could occur to operationalize SSRL research. Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning. Provides clear suggestions for future human‐AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills. Implications for practice and/or policy Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning. Researchers and practitioners could benefit from methodological development incorporating human‐AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

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“…Järvelä et al (2023), pose new ways of thinking about the integration of human and AI collaboration in socially shared regulation (SSRL). The authors explore the intersection of human and AI collaboration in SSRL research.…”

“…In so doing, they collectively raise questions about the applicability of traditional learning theories in an education context increasingly powered and influenced by non-human agents. Järvelä et al (2023), pose new ways of thinking about the integration of human and AI collaboration in socially shared regulation (SSRL). The authors explore the intersection of human and AI collaboration in SSRL research.…”

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“…The research on how the collaboration of AI and humans advances human capabilities of decision-making, performing, and goal attainment are still emerging (Akata et al, 2020). Molenaar (2022) and Järvelä et al (2023) proposed models to illustrate how humans collaborate with AI in learning regulation. Molenaar (2022) described the use of adaptive learning technologies in developing SRL skills in a Hybrid Human-AI Regulation system.…”

“…The agency of regulation then transitions back from AI to humans to enable self-regulation with reduced support from AI. Järvelä et al (2023) further expanded the "detect-diagnose-act" functions of AI in learning regulation that Molenaar (2022) described by incorporating learning ability of AI to adapt scaffolds to human learning regulation.…”