Design of an AI-Driven Collaborative Learning Model for Enhancing Peer Interactions and Knowledge Sharing in Online Education

Abstract

Online collaborative learning communities are susceptible to the problems of lack of peer interaction, skewed participation, and poor knowledge sharing, which adversely affect learning outcomes. The recent developments in artificial intelligence (AI) provide the opportunity to solve these problems with the help of adaptive learning analytics and intelligent collaboration support. The paper suggests an AI-based Collaborative Learning Model (AI-CLM) that is aimed at improving peer interactions and sharing of knowledge during an online study. The suggested model combines the analysis of the interaction of the learners, peer grouping using AI, adaptive feedback, and knowledge recommendation in one framework. It utilizes a systematic algorithm and mathematically formulated evaluation measures to guarantee reproducibility and stringent evaluation. An online collaborative learning course of eight weeks has been evaluated using an experimental mode and showed that the proposed AI-CLM not only performs significantly higher than the traditional collaborative learning methods. Precisely, the degrees of participation boosted since the Peer Interaction Index grew by 12.4 to 21.8, the Knowledge Sharing Score rose by 68.2 to 84.6, and the Participation Balance Index rose by 0.54 to 0.81, which is more equitable learner participation. Moreover, the average learning performance increased to 85.3 percent as compared to 72.5 percent, and the Collaboration Effectiveness Score also increased to 0.86 compared to 0.63. These findings can be used to conclude that AI-based adaptive collaboration support can significantly enhance social interaction and academic performance during online learning. These results can be relevant to the development of scalable, smart online learning systems that facilitate efficient collaboration and interaction with a learner.