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The rapid integration of Artificial Intelligence (AI) in Open and Distance Learning (ODL) presents both transformative opportunities and significant ethical challenges. This study aims to address the critical issue of ensuring equity and integrity in AI-driven academic performance predictions. By developing a Structural Equation Modeling (SEM) based process framework, the research ensures that AI tools do not perpetuate biases or inequalities. The study employs a mixed-methods approach, incorporating quantitative survey data and qualitative interviews with ODL students. The instruments were validated using expert reviews and reliability tests, achieving a Cronbach’s alpha of 0.70. Results indicate the framework’s high accuracy and fairness, with a Comparative Fit Index (CFI) of 1.000 and a Root Mean Square Error of Approximation (RMSEA) of 0.000. Empirical evidence demonstrates that AI frameworks support educational equity by enabling data-driven interventions. However, concerns such as data privacy and the digital divide remain. This research contributes to the academic discourse by proposing solutions for ethical AI deployment in education. Future research should focus on refining AI frameworks to ensure scalability across diverse educational settings while continuously addressing equity, privacy, and inclusivity.

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Ensuring Equity and Integrity in Academic Performance Prediction in Open and Distance Learning

  • 章节

  • 发表日期：

    2025年

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  • Muyideen D. Adewale,
  • Ambrose A. Azeta,
  • Steven Tjiraso

摘要

The rapid integration of Artificial Intelligence (AI) in Open and Distance Learning (ODL) presents both transformative opportunities and significant ethical challenges. This study aims to address the critical issue of ensuring equity and integrity in AI-driven academic performance predictions. By developing a Structural Equation Modeling (SEM) based process framework, the research ensures that AI tools do not perpetuate biases or inequalities. The study employs a mixed-methods approach, incorporating quantitative survey data and qualitative interviews with ODL students. The instruments were validated using expert reviews and reliability tests, achieving a Cronbach’s alpha of 0.70. Results indicate the framework’s high accuracy and fairness, with a Comparative Fit Index (CFI) of 1.000 and a Root Mean Square Error of Approximation (RMSEA) of 0.000. Empirical evidence demonstrates that AI frameworks support educational equity by enabling data-driven interventions. However, concerns such as data privacy and the digital divide remain. This research contributes to the academic discourse by proposing solutions for ethical AI deployment in education. Future research should focus on refining AI frameworks to ensure scalability across diverse educational settings while continuously addressing equity, privacy, and inclusivity.