Empowering Peer Tutoring via AI-Driven Refinery Intervention Treatment in Education

Abstract

Refinery intervention treatment via peer tutoring has gained recognition as an effective approach for enhancing student learning and academic achievement. This theoretical paper examines the manual processes involved in conducting peer tutoring sessions, identifies challenges in its implementation, and proposes an automation to streamline the process. This paper emphasizes the benefits of automating the peer tutoring process, including improved efficiency, and creating opportunities for personalized and adaptive learning experiences. Acknowledging the transformative potential of technology in education, the study advocated for the implementation of automated systems in peer tutoring. The system leverages machine learning, data analytics, and artificial intelligence to optimize tutor-student matching, generate personalized tutoring plans, and provide real-time progress tracking. By automating operational tasks, educators and peer leaders can focus on facilitating meaningful interactions and delivering targeted instructional support. While recognizing the potential challenges and ethical considerations associated with automation, this paper emphasizes the importance of striking a balance between technological advancements and human engagement. The transition from manual arrangements to automated systems in peer tutoring has the potential to revolutionize educational practices, ensuring that every student benefits from peer support and promoting a culture of collaborative learning. By embracing automation, this study can harness the power of technology to enhance the effectiveness and accessibility of peer tutoring, ultimately improving student outcomes and fostering a dynamic learning environment.