Development of AI-Based Predictive Models for University Student Academic Probation Risks: A Case Study of Federal University, Wukari (Wukari LGA, Taraba State)
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Background of the Study
University student performance is a critical factor in ensuring academic success, both for the students themselves and for the institution as a whole. One of the key concerns in higher education is the identification and intervention of students at risk of academic failure, which could result in academic probation (Oluwaseun et al., 2024). Traditionally, academic probation is determined by a student’s grades at the end of a semester, without the benefit of predictive analytics that could identify at-risk students earlier in their academic journey (Anwar & Ibrahim, 2023). However, recent advances in AI have provided new opportunities for predicting student success and identifying those at risk of probation through predictive modeling techniques (Fatima et al., 2025). These models can analyze historical data, such as grades, attendance, and engagement levels, to predict which students may be at risk of underperforming, allowing for early intervention and support.
Federal University, Wukari, located in Wukari LGA, Taraba State, provides an ideal case study for developing and testing AI-based predictive models for academic probation risks. The university, like many others, faces challenges related to student retention and performance monitoring. By implementing AI-driven predictive models, the university could proactively identify students at risk and offer timely interventions, thereby improving student retention rates and academic outcomes.
Statement of the Problem
At Federal University, Wukari, many students face academic probation due to low grades or lack of engagement, but the university’s current methods of identifying at-risk students are reactive and often occur too late for effective intervention. While academic probation is used as a measure to encourage academic improvement, there is a lack of predictive systems to identify students who may need assistance before it is too late. AI-based predictive models have the potential to address this gap, but the effectiveness of these models in identifying academic probation risks and guiding early interventions at the university is yet to be explored.
Objectives of the Study
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To develop AI-based predictive models for identifying students at risk of academic probation at Federal University, Wukari.
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To evaluate the effectiveness of AI-based predictive models in early identification and intervention for at-risk students.
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To assess the potential of AI-driven predictive models in improving student retention and academic performance at the university.
Research Questions
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How accurate are AI-based predictive models in identifying students at risk of academic probation at Federal University, Wukari?
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What impact does early identification of at-risk students have on their academic performance and retention rates?
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How feasible is the implementation of AI-based predictive models for academic probation in Nigerian universities?
Significance of the Study
This study will provide valuable insights into how AI-based predictive models can be used to proactively identify students at risk of academic failure, leading to early interventions and improved student outcomes. The findings could have broader implications for universities seeking to improve retention and academic success rates through data-driven approaches.
Scope and Limitations of the Study
This study will focus on the development and evaluation of AI-based predictive models for identifying academic probation risks at Federal University, Wukari, located in Wukari LGA, Taraba State. The study will be limited to the assessment of predictive model accuracy and its impact on student retention, excluding other academic support systems.
Definitions of Terms
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AI-Based Predictive Models: Algorithms and statistical techniques that use historical data to forecast future outcomes, such as student performance and risks.
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Academic Probation: A status given to students who do not meet the required academic standards, usually based on GPA or course completion rates.
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Student Retention: The ability of a university to keep students enrolled and engaged in their academic programs, preventing dropouts or failures.
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