The effect of artificial intelligence as a peer-to- peer support tool on engagement, grades and pass rates peer-to-peer

Abstract

The study examines artificial intelligence’s (AI) role in peer-topeer 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 peerlearning environments. Partnership with AI developers remains crucial to integrate technology momentum with teaching targets. All AI peer-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. Bottom of Form