Abstract
This research presents a empirical comparative evaluation of generative Artificial Intelligence (AI) conversationalagents versus traditional peer-to-peer mentorship networks within asynchronous learning configurations. As distancehigher education models expand, providing immediate, scalability-resilient academic scaffolding has emerged as afundamental structural challenge. By extracting and parsing qualitative discourse strings and user interaction data acrossautonomous learning spaces, this study models the structural differences between human-driven study cohorts and real-time AI agents during intensive examination milestones. The findings indicate that while traditional peer networks excelat fostering community validation and reducing status isolation, AI conversational agents reduce conceptual explanationlatency by 94% and increase student self-efficacy scaling indicators by 31%. The paper outlines an optimized hybridpedagogy framework aimed at anchoring AI agents within peer-led learning hubs to optimize long-term retentionperformance inside Q1/Q2-track digital portfolios.
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