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

In Outcome-Based Education (OBE) environments, student academic progressionrepresents a complex, non-linear dynamic system governed by multi-faceted indicatorsincluding performance trajectories, engagement metrics, and learningsupport mechanisms. Traditional evaluation methods often fail to capture time-dependentperformance risks and degradation patterns across successive assessmentstages. In this paper, we establish a novel reliability-based degradationmodeling framework that adapts engineering reliability principles and hazard-ratesurvival analysis to educational decision-support systems. Utilizing a standardized,publicly available academic progress dataset, our framework modelsperformance stability via cumulative reliability functions and identifies critical vulnerabilitythresholds using hazard-rate formulations. Students are classified intodistinct progression cohorts (high, moderate, and low achievers) based on normalizedperformance distributions. Empirical findings demonstrate a strong correlationbetween early academic stability and long-term progression reliability,showing that multi-year failing trajectories exhibit exponentially higher degradationrates. The proposed framework provides an interpretable, data-driven analyticaltool to monitor student risk, support early Artificial Intelligence (AI)-driveninterventions, and optimize institutional decision-making in modern outcome-based learning architectures.