This paper presents a novel deep learning architecture for item response time prediction to create human readable feedback for content creators in educational assessment. The model employs a two-tier attention mechanism, mirroring the cognitive hierarchy of reading: the first tier identifies influential words nested within their sentence context, while the second tier evaluates the contribution of individual sentences to the overall item block. By leveraging this specific hierarchical structure, the model improves explainable AI (xAI) in educational measurement AI research. While architecturally simpler models can achieve comparable or superior raw predictive accuracy, the two-tier structure is designed specifically for xAI — to yield transparent, sentence- and world-level attribution that item developers can act on directly. This methodology bridges the gap between opaque, black-box predictive modeling and actionable, human interpretable feedback for instructional design and item development.