Harnessing AI for Peer-to-Peer Learning Support: Insights from a Bibliometric Analysis
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| Title: | Harnessing AI for Peer-to-Peer Learning Support: Insights from a Bibliometric Analysis |
|---|---|
| Language: | English |
| Authors: | Mark Wilson-Trollip (ORCID |
| Source: | Perspectives in Education. 2024 42(4):283-304. |
| Availability: | University of the Free State Faculty of Education. P.O. Box 339, Bioemfontein 9300, South Africa. Tel: +27-51-401-2368; e-mail: PiE@ufs.ac.za; Web site: https://journals.ufs.ac.za/index.php/pie/index |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Artificial Intelligence, Peer Teaching, Peer Relationship, Learning Theories, Computer Software, Technology Uses in Education, Social Emotional Learning, Ethics, Research Needs, Information Retrieval, Bibliometrics |
| DOI: | 10.38140/pie.v42i4.8431 |
| ISSN: | 0258-2236 2519-593X |
| Abstract: | This study investigates the current use of artificial intelligence (AI) for peer-to-peer support through a bibliometric analysis of 1113 records based on co-words. The research employs a four-phase exploratory design that involves co-word search, data charting, and result summation. It uses an inductive, relevant, and reflexive thematic approach (TA). The theoretical foundation is grounded in retention, attrition, and learning theories, including personal, social, emotional, constructive, and humanistic theories. The analysis aims to determine whether institutions leverage AI for peer-to-peer learning as part of peer support. The findings reveal a significant gap in direct research on AI's role in peer-to-peer support, underscoring the need for future studies on its impact on learning customisation, socio-emotional learning, and ethical issues in education. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1453545 |
| Database: | ERIC |
| Abstract: | This study investigates the current use of artificial intelligence (AI) for peer-to-peer support through a bibliometric analysis of 1113 records based on co-words. The research employs a four-phase exploratory design that involves co-word search, data charting, and result summation. It uses an inductive, relevant, and reflexive thematic approach (TA). The theoretical foundation is grounded in retention, attrition, and learning theories, including personal, social, emotional, constructive, and humanistic theories. The analysis aims to determine whether institutions leverage AI for peer-to-peer learning as part of peer support. The findings reveal a significant gap in direct research on AI's role in peer-to-peer support, underscoring the need for future studies on its impact on learning customisation, socio-emotional learning, and ethical issues in education. |
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| ISSN: | 0258-2236 2519-593X |
| DOI: | 10.38140/pie.v42i4.8431 |
