The DOI matches its Crossref record; 61 of the 65 references it cites were found in a registry or an index, and 4 are not checked yet.
Human and artificial intelligence collaboration for socially shared regulation in learning
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Metacognition in Educational Theory and Practice
Hacker, D. J., Dunlosky, J. T., & Graesser, A. C. (1998) · Routledge eBooks · doi:10.4324/9781410602350 · Cited by 2,138
Handbook of Self-Regulation of Learning and Performance
(2017) · Routledge eBooks · doi:10.4324/9781315697048 · Cited by 1,977
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
Evolution and Revolution in Artificial Intelligence in Education
Roll, I., & Wylie, R. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0110-3 · Cited by 1,229
Facial expression recognition from near-infrared videos
Zhao, G., Huang, X., Taini, M., Li, S. Z., & Pietikäinen, M. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.07.002 · Cited by 771
Designing educational technologies in the age of AI: A learning sciences‐driven approach
Luckin, R., & Cukurova, M. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12861 · Cited by 694
Self-Regulation, Co-Regulation, and Shared Regulation in Collaborative Learning Environments
Hadwin, A. F., Järvelä, S., & Miller, M. (2017) · Routledge eBooks · doi:10.4324/9781315697048-6 · Cited by 683
Self-citationHigh-level co-regulation in collaborative learning: How does it emerge and how is it sustained?
Volet, S., Summers, M., & Thurman, J. (2009) · Learning and Instruction · doi:10.1016/j.learninstruc.2008.03.001 · Cited by 466
Recognising spontaneous facial micro-expressions
Pfister, T., Li, X., Zhao, G., & Pietikainen, M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126401 · Cited by 450
Flagged first, then by citations
Research works printed but not linked: 5
- Nguyen A., 2022, Hybrid human‐AI shared regulation in learning (HASRL) model
- Nguyen A., 2023, Theory informing and arising from learning analytics
- Nguyen A., 2023, Emotional synchrony through regulatory triggers in collaborative learning
- Nguyen A., 2022, Exploring socially shared regulation with an AI deep learning approach using multimodal data, Proceedings of International Conferences of Learning Sciences (ICLS)
- Schunk D. H., 2018, Handbook of self‐regulation of learning and performance, 1
Cited by
Sorted by citations
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805
Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548
The promise and challenges of generative AI in education
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2025) · Behaviour & Information Technology · doi:10.1080/0144929x.2024.2394886 · Cited by 543
Self-citationHuman-AI collaboration patterns in AI-assisted academic writing
Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2323593 · Cited by 449
“In order to capture the complexities of these human-AI collaborative processes, we employ an AI-driven learning analytics approach (Järvelä, Nguyen, and Hadwin 2023; Ouyang, Xu, and Cukurova 2023) that allows for the computational extraction and interpretation of rich behavioural data during…”
BackgroundMethodResultSelf-citationChatGPT improves creative problem-solving performance in university students: An experimental study
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105031 · Cited by 396
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
