Background of the Study
Collaborative learning and peer-to-peer interactions are fundamental to the educational experience, particularly at the university level. In the era of digital learning, AI-based platforms have emerged to facilitate and optimize student collaboration. These platforms leverage machine learning algorithms to match students with compatible peers, promote knowledge sharing, and foster collaborative problem-solving. Such systems are particularly valuable in fostering a deeper understanding of subject matter and encouraging independent learning.
Federal University, Kashere, Gombe State, has a growing population of students, with increasing reliance on digital platforms for both learning and communication. However, despite the presence of online discussion forums and learning management systems, there is a lack of AI-based systems that effectively optimize peer learning and collaboration among students. This research aims to optimize an AI-based student collaboration and peer-learning platform that can enhance student interactions, improve learning outcomes, and facilitate knowledge transfer among students at Federal University, Kashere.
AI technologies can enhance peer-learning platforms by intelligently grouping students based on their learning styles, strengths, and academic performance. Additionally, AI can facilitate real-time feedback, track engagement, and personalize collaboration opportunities. However, the effectiveness of such platforms in enhancing student collaboration in the Nigerian context, especially at Federal University, Kashere, has not been extensively explored. This study will focus on the development and optimization of an AI-based platform that can support and enhance student collaboration and peer learning in the university.
Statement of the Problem
Despite the increasing reliance on digital platforms for student interaction and collaboration, Federal University, Kashere, lacks an AI-based system that optimizes peer learning and enhances collaboration. Current platforms do not leverage AI to tailor the learning experience or match students based on their strengths and learning preferences. Without such optimization, students may miss out on opportunities for effective collaboration, hindering their academic performance and peer interactions. This research aims to fill this gap by developing an AI-based platform designed to optimize student collaboration and peer learning.
Objectives of the Study
Research Questions
Research Hypotheses
Significance of the Study
This study will provide valuable insights into the use of AI in optimizing student collaboration and peer learning at Nigerian universities. The findings will contribute to improving student engagement, learning outcomes, and the overall academic experience at Federal University, Kashere. The research could also serve as a model for other universities seeking to enhance peer learning through AI technologies.
Scope and Limitations of the Study
This study will focus on the development and optimization of an AI-based student collaboration and peer-learning platform at Federal University, Kashere, Gombe State. It will assess the platform’s effectiveness in enhancing student interaction and academic outcomes within the context of this particular institution. The study is limited to Federal University, Kashere, and may not reflect the broader challenges faced by other institutions in Nigeria.
Definitions of Terms
AI-Based Student Collaboration Platform: A digital platform that uses AI technologies to optimize student interactions, peer learning, and collaborative problem-solving based on individual learning styles and academic needs.
Peer Learning: A learning process where students collaborate with each other, sharing knowledge and helping each other understand subject matter.
Federal University, Kashere: A higher education institution located in Kashere, Gombe State, Nigeria.
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