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Human and artificial intelligence collaboration for socially shared regulation in learning
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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 notes What 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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“this study points out an imperative to devise strategic methodologies for effective human-AI collaboration to integrate such AI technologies into academic writing in higher education.”
From Human-AI collaboration patterns in AI-assisted academic writing (2024) · cites this paper as Järvelä, Nguyen, and Hadwin 2023
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Metadata
- Authors
- Järvelä, S., Nguyen, A., & Hadwin, A.
- Affiliations
- University of Oulu (FI)
- University of Victoria (CA)
- Corresponding author
- Järvelä, Sanna
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2023-04-20
- Volume
- 54
- Issue
- 5
- Pages
- 1057-1076
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W4366588310
- Cited by
- 342
- References
- 61
- Citations by year
- 2026: 145 · 2025: 120 · 2024: 61 · 2023: 16
- FWCI
- 165.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Educational Technology (publishedVersion, cc-by)
- UVic’s Research and Learning Repository (University of Victoria) (submittedVersion, cc-by)
- Funders
- Academy of Finland (324381, 350249)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- human-AI collaboration, socially shared regulation, learning regulation, hybrid intelligence, AI affordances, adaptive learning support, cooperative learning, multimodal analysis
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