Artificial Intelligence-Enabled Reliability and Hazard-RateDegradation Modeling for Dynamic Student Progression inOutcome-Based Educational Systems

Abstract

In Outcome-Based Education (OBE) environments, student academic progression
represents a complex, non-linear dynamic system governed by multi-faceted indicators
including performance trajectories, engagement metrics, and learning
support mechanisms. Traditional evaluation methods often fail to capture time-dependent
performance risks and degradation patterns across successive assessment
stages. In this paper, we establish a novel reliability-based degradation
modeling framework that adapts engineering reliability principles and hazard-rate
survival analysis to educational decision-support systems. Utilizing a standardized,
publicly available academic progress dataset, our framework models
performance stability via cumulative reliability functions and identifies critical vulnerability
thresholds using hazard-rate formulations. Students are classified into
distinct progression cohorts (high, moderate, and low achievers) based on normalized
performance distributions. Empirical findings demonstrate a strong correlation
between early academic stability and long-term progression reliability,
showing that multi-year failing trajectories exhibit exponentially higher degradation
rates. The proposed framework provides an interpretable, data-driven analytical
tool to monitor student risk, support early Artificial Intelligence (AI)-driven
interventions, and optimize institutional decision-making in modern outcome-based learning architectures.