Measuring AI Value Beyond Accuracy Metrics in Academia

Abstract

Artificial intelligence systems are increasingly em bedded in academic research, teaching, evaluation, and scholarly infrastructure. Despite this growth, the value of such systems is often assessed using narrow accuracy focused metrics that do not fully reflect their academic impact. This paper examines how AI value in academia can be measured beyond predictive accuracy by incorporating dimensions such as interpretability, trust, human alignment, governance, sustainability, and scholarly outcomes. A multi dimensional evaluation framework is proposed and empirically explored through simulated academic scenarios. The findings demonstrate that accuracy alone is insufficient to capture the real contribution of AI systems in academic environments and that broader value metrics are essential for responsible and effective adoption.