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Socially Shared Regulation of Learning and Artificial Intelligence: Opportunities to Support Socially Shared Regulation

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Title: Socially Shared Regulation of Learning and Artificial Intelligence: Opportunities to Support Socially Shared Regulation
Language: English
Authors: Jinhee Kim (ORCID 0000-0002-3365-7354), Rita Detrick, Seongryeong Yu, Yukyeong Song, Linda Bol, Na Li (ORCID 0000-0003-2395-3499)
Source: Education and Information Technologies. 2025 30(9):11483-11521.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 39
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Electronic Learning, Cooperative Learning, Student Attitudes, College Students, Learning Processes, Metacognition, Learning Motivation, Pedagogical Content Knowledge, Technological Literacy
DOI: 10.1007/s10639-024-13187-9
ISSN: 1360-2357
1573-7608
Abstract: Supporting learners in achieving high-level socially shared regulation of learning (SSRL) in the online collaborative learning (OCL) context presents challenges that the utilization of artificial intelligence (AI) technologies may help solve. However, the effective uses of AI to support multifaceted areas (cognition, metacognition, and motivation) and phases (forethought, performance, and reflection) of SSRL remain elusive. Furthermore, research on developing an educational AI and what pedagogical attributes and elements are required for AI to support students' SSRL effectively is limited. This study, therefore, aims to investigate students' perceptions of AI applications in enhancing SSRL and to explore the essential pedagogical elements necessary for AI to support SSRL during the OCL. To achieve these aims, the study conducted Focus Group Interviews facilitated by 9 scenarios of AI application storyboards and paper prototypes with 30 undergraduate and graduate students. The study findings show that students perceive various types of AI to support cognitive, metacognitive, and motivational areas across different SSRL phases. The study also found that students viewed AI as an active learning agent, serving in roles previously inhabited solely by human educators and students. Furthermore, the study reveals seven key pedagogical elements across TPACK components such as pedagogical, content, technological, pedagogical content, technological pedagogical, technological content, and technological pedagogical content knowledge deemed crucial by students for AI to support SSRL in OCL effectively. These findings offer implications for using and designing educationally relevant AI to support SSRL in OCL environments.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1475276
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Jinhee+Kim%22">Jinhee Kim</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3365-7354">0000-0002-3365-7354</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rita+Detrick%22">Rita Detrick</searchLink><br /><searchLink fieldCode="AR" term="%22Seongryeong+Yu%22">Seongryeong Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Yukyeong+Song%22">Yukyeong Song</searchLink><br /><searchLink fieldCode="AR" term="%22Linda+Bol%22">Linda Bol</searchLink><br /><searchLink fieldCode="AR" term="%22Na+Li%22">Na Li</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2395-3499">0000-0003-2395-3499</externalLink>)
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Supporting learners in achieving high-level socially shared regulation of learning (SSRL) in the online collaborative learning (OCL) context presents challenges that the utilization of artificial intelligence (AI) technologies may help solve. However, the effective uses of AI to support multifaceted areas (cognition, metacognition, and motivation) and phases (forethought, performance, and reflection) of SSRL remain elusive. Furthermore, research on developing an educational AI and what pedagogical attributes and elements are required for AI to support students' SSRL effectively is limited. This study, therefore, aims to investigate students' perceptions of AI applications in enhancing SSRL and to explore the essential pedagogical elements necessary for AI to support SSRL during the OCL. To achieve these aims, the study conducted Focus Group Interviews facilitated by 9 scenarios of AI application storyboards and paper prototypes with 30 undergraduate and graduate students. The study findings show that students perceive various types of AI to support cognitive, metacognitive, and motivational areas across different SSRL phases. The study also found that students viewed AI as an active learning agent, serving in roles previously inhabited solely by human educators and students. Furthermore, the study reveals seven key pedagogical elements across TPACK components such as pedagogical, content, technological, pedagogical content, technological pedagogical, technological content, and technological pedagogical content knowledge deemed crucial by students for AI to support SSRL in OCL effectively. These findings offer implications for using and designing educationally relevant AI to support SSRL in OCL environments.
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