Automated essay scoring (AES) has revolutionized education. But what if these systems could explain their grades? New research introduces "Reasoning Distillation-Based Evaluation," or RDBE, an innovative approach to scoring student writing. Imagine an AI not just assigning a number but explaining the "why" behind it, offering valuable insights into writing quality. RDBE does just that—it teaches smaller AI models to provide not only scores but also detailed reasoning for their evaluations. This is achieved by “distilling” knowledge from large language models (LLMs) about essay scoring rubrics and using it to fine-tune the smaller model. The smaller model learns to reason and interpret like the LLM, making its scores more transparent and understandable. Tested on the DREsSNew dataset, RDBE outperforms existing methods, proving its ability to enhance automated scoring and offer valuable feedback to both students and educators. While more research is needed (especially with higher quality data generation), this innovative technique opens doors to a new era of explainable AI in education, where machines provide scores and illuminate the path to better writing.