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Case Study on Llama-Based AI Tutors for Computer Science Education in Remote Areas
Abstract
In remote and underserved locations, there is a serious education crisis because of the low number of trained educators and learning opportunities to provide quality computer science instruction. The present case study focuses on the way that Llama-based AI tutors impacted more than 370 students with the help of hybrid learning, showing considerable advances in the solving of assignments and the improvements of the learning outcomes. The research is based on the theory of Zone of Proximal Development introduced by Vygotsky and investigates the influence of AI-based instructional support on the scope of knowledge and developing inclusive learning patterns. These results are supported by quantitative data and qualitative feedback, which shows that AI tutors have the ability to maximize student engagement and performance as well as areas of improvement. The results are crucial in the understanding of the transformative nature of AI in the education process and human-AI partnership in the classroom.
PCF11 Sub-Theme: Gender, Technology and Innovation in Open Education
Paper ID: 3988
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2025-09
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Commonwealth of Learning (COL)