International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 732KB)
Edge-First Adaptive Learning with Lightweight RL and LNN for STEM Education in Low- Resource Kenyan Schools
PDF (732KB), PP.71-88
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Author(s)
Index Terms
Adaptive Learning, Artificial Intelligence, STEM Education, Resource-Constraint Schools, Reinforcement Learning, Educational Technology, Kenya
Abstract
There’s a lot of promise around artificial intelligence for education to personalize learning; however, there has been very little research regarding Artificial Intelligence (AI) applications in fields with very few resources for its implementation. This paper describes a proposed AI-based adaptive learning system that aims to personalize STEM education in a low-resource school environment in Kenya. This research addresses the numerous challenges associated with such a system, such as irregular internet access, limited computer hardware in situ and no previous teacher background in both AI and education. To address these issues, an adapted reinforcement learning algorithm will personalize the content shown to students, and a modified liquid neural network is used for the prediction of student success, while not being computationally expensive. As compared to traditional adaptive systems, this adaptive learning platform supports edge computing and offline updates in order to operate in a consistently low connectivity environment. In addition, by continually adjusting the difficulty, format, and rate of delivery of STEM topics to fit the style, prior knowledge, and attention level of the individual student, this platform has been seen to improve the educational experience. An 8-month case study was done with 6 Kenyan low-resource schools and 6 comparison schools located in the city. In this case, we report a 31% increase in the level of understanding of students’ key STEM subjects, 27% reduction in student drop rate from STEM topics, and 43% increase in teacher efficiency over traditional methods. We were also able to predict the level of performance of students to a 89% success while occupying a low 1.9MB memory, making it feasible to be employed on budget Android devices. This study presents evidence that the utilization of AI for personalized adaptive learning technologies in order to minimize the disparities in the provision of STEM education in low-resource settings worldwide is possible.
Cite This Paper
David Shiala Ongoma, “Edge-First Adaptive Learning with Lightweight RL and LNN for STEM Education in Low-Resource Kenyan Schools”, International Journal of Education and Management Engineering (IJEME), Vol.16, No.4, pp. 71-88, 2026. DOI:10.5815/ijeme.2026.04.06
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