The Effect of Artificial Intelligence as a Peer-to-Peer Support Tool on Engagement, Grades and Pass Rates Peer-to-Peer
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| Title: | The Effect of Artificial Intelligence as a Peer-to-Peer Support Tool on Engagement, Grades and Pass Rates Peer-to-Peer |
|---|---|
| Language: | English |
| Authors: | Mark Wilson-Trollip (ORCID |
| Source: | Perspectives in Education. 2025 43(1):239-266. |
| 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: | 28 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Peer Teaching, Learner Engagement, Academic Achievement, Student Improvement, At Risk Students, Prediction, Educational Technology, Technology Uses in Education, College Students, Grades (Scholastic) |
| DOI: | 10.38140/pie.v43i1.8911 |
| ISSN: | 0258-2236 2519-593X |
| Abstract: | The study examines artificial intelligence's (AI) role in peer-to-peer learning and its impact on student engagement, academic performance, and pass rates. The research provided insights into how AI influences student engagement and grades through a mixed-methods approach. The research methodology adopted an interpretivist approach that includes aspects of positivism. The design employs induction using qualitative and quantitative methods. The case study operates at a longitudinal time range to study temporal performance developments. The primary data collection uses analysis of external data. The findings indicate a slight improvement in grades (3-5%), lacking statistical significance (p > 0.05) and a significant level of engagement (95%). This statistic raises questions about the relationship between increased engagement and tangible academic outcomes. Students show enhanced engagement through AI-driven personalised learning pathways and continuous feedback systems. Through predictive AI systems, institutions can immediately identify students facing performance-related challenges. AI peer-to-peer support must become accessible to every student without restricting it to only at-risk students. AI application policies in universities must follow mechanisms that allow adjustment for various student peer-learning environments. Partnership with AI developers remains crucial to integrate technology momentum with teaching targets. All AI pteer-support policies need to include ethical criteria to maintain responsible deployment. The results suggest that while AI platforms can complement traditional peer support services, further research is necessary to understand their long-term effects on academic performance and retention. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1478182 |
| Database: | ERIC |
| Abstract: | The study examines artificial intelligence's (AI) role in peer-to-peer learning and its impact on student engagement, academic performance, and pass rates. The research provided insights into how AI influences student engagement and grades through a mixed-methods approach. The research methodology adopted an interpretivist approach that includes aspects of positivism. The design employs induction using qualitative and quantitative methods. The case study operates at a longitudinal time range to study temporal performance developments. The primary data collection uses analysis of external data. The findings indicate a slight improvement in grades (3-5%), lacking statistical significance (p > 0.05) and a significant level of engagement (95%). This statistic raises questions about the relationship between increased engagement and tangible academic outcomes. Students show enhanced engagement through AI-driven personalised learning pathways and continuous feedback systems. Through predictive AI systems, institutions can immediately identify students facing performance-related challenges. AI peer-to-peer support must become accessible to every student without restricting it to only at-risk students. AI application policies in universities must follow mechanisms that allow adjustment for various student peer-learning environments. Partnership with AI developers remains crucial to integrate technology momentum with teaching targets. All AI pteer-support policies need to include ethical criteria to maintain responsible deployment. The results suggest that while AI platforms can complement traditional peer support services, further research is necessary to understand their long-term effects on academic performance and retention. |
|---|---|
| ISSN: | 0258-2236 2519-593X |
| DOI: | 10.38140/pie.v43i1.8911 |
