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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 0009-0001-4219-3125)
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
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  Data: 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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  Data: <searchLink fieldCode="AR" term="%22Mark+Wilson-Trollip%22">Mark Wilson-Trollip</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-4219-3125">0009-0001-4219-3125</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Perspectives+in+Education%22"><i>Perspectives in Education</i></searchLink>. 2025 43(1):239-266.
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  Data: 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
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+Teaching%22">Peer Teaching</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Improvement%22">Student Improvement</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Grades+%28Scholastic%29%22">Grades (Scholastic)</searchLink>
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  Data: 10.38140/pie.v43i1.8911
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Peer Teaching
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      – SubjectFull: Learner Engagement
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      – SubjectFull: Academic Achievement
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      – SubjectFull: Student Improvement
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      – SubjectFull: At Risk Students
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      – SubjectFull: Prediction
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      – SubjectFull: Educational Technology
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: College Students
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      – SubjectFull: Grades (Scholastic)
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