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Artificial Intelligence (AI) is increasingly being leveraged to predict undergraduate degree completion, with the goal of enhancing educational outcomes and student support services. This study systematically reviews the AI techniques employed in this domain and explores how student characteristics have been effectively linked to academic success. Following PRISMA guidelines, a systematic literature review was conducted using the Scopus database. Out of 221 peer-reviewed articles initially identified, 51 were deemed relevant after screening. The findings reveal that a variety of AI methods drawing on academic performance, demographic factors, and behavioral data have been successfully applied to forecast degree completion among undergraduates.

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A Systematic Review of Artificial Intelligence in Predicting Undergraduate Degree Completion

  • 会议论文

  • 发表日期：

    2026年

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  • Lizzy Ofusori,
  • Tebogo Bokaba,
  • Eustace M. Dogo

摘要

Artificial Intelligence (AI) is increasingly being leveraged to predict undergraduate degree completion, with the goal of enhancing educational outcomes and student support services. This study systematically reviews the AI techniques employed in this domain and explores how student characteristics have been effectively linked to academic success. Following PRISMA guidelines, a systematic literature review was conducted using the Scopus database. Out of 221 peer-reviewed articles initially identified, 51 were deemed relevant after screening. The findings reveal that a variety of AI methods drawing on academic performance, demographic factors, and behavioral data have been successfully applied to forecast degree completion among undergraduates.