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As Artificial Intelligence (AI) becomes increasingly integral to education, particularly in Open and Distance Learning (ODL), educators, policymakers, and AI developers need to understand its extensive effects. Harnessing AI’s full potential in ODL requires an intricate understanding of its multi-faceted implications—a challenge taken up by this research. This research aims to introduce a Multilayered Process Framework for Predicting Students’ Academic Performance in Open and Distance Learning. The framework’s design progresses from theoretical modelling to advanced predictive analytics, with each layer building on the previous to ensure a cohesive analytical process. It incorporates rigorous validation checks to maintain predictive accuracy and reliability. The study culminates in a comparative analysis to identify the most effective predictive methodology, emphasising the framework’s dynamic adaptability and commitment to continuous improvement based on empirical evidence and theoretical insights. Aligned with UNESCO’s 2030 vision, it underscores the framework’s versatility across different educational landscapes and its commitment to inclusive education. It paves the way for future research focused on leveraging AI to universalise quality education, highlighting the potential of AI to transform educational technology.

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A Multilayered Process Framework for Predicting Students’ Academic Performance in Open and Distance Learning

  • 会议论文

  • 发表日期：

    2025年

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  • M. D. Adewale,
  • A. Azeta,
  • A. Abayomi-Alli,
  • A. Sambo-Magaji

摘要

As Artificial Intelligence (AI) becomes increasingly integral to education, particularly in Open and Distance Learning (ODL), educators, policymakers, and AI developers need to understand its extensive effects. Harnessing AI’s full potential in ODL requires an intricate understanding of its multi-faceted implications—a challenge taken up by this research. This research aims to introduce a Multilayered Process Framework for Predicting Students’ Academic Performance in Open and Distance Learning. The framework’s design progresses from theoretical modelling to advanced predictive analytics, with each layer building on the previous to ensure a cohesive analytical process. It incorporates rigorous validation checks to maintain predictive accuracy and reliability. The study culminates in a comparative analysis to identify the most effective predictive methodology, emphasising the framework’s dynamic adaptability and commitment to continuous improvement based on empirical evidence and theoretical insights. Aligned with UNESCO’s 2030 vision, it underscores the framework’s versatility across different educational landscapes and its commitment to inclusive education. It paves the way for future research focused on leveraging AI to universalise quality education, highlighting the potential of AI to transform educational technology.