  Providing explainable and faithful feedback is crucial for automated student
answer assessment. In this paper, we introduce a novel framework that explores
using ChatGPT, a cutting-edge large language model, for the concurrent tasks of
student answer scoring and rationale generation. We identify the appropriate
instructions by prompting ChatGPT with different templates to collect the
rationales, where inconsistent rationales are refined to align with marking
standards. The refined ChatGPT outputs enable us to fine-tune a smaller
language model that simultaneously assesses student answers and provides
rationales. Extensive experiments on the benchmark dataset show that the
proposed method improves the overall QWK score by 11% compared to ChatGPT.
Furthermore, our thorough analysis and human evaluation demonstrate that the
rationales generated by our proposed method are comparable to those of ChatGPT.
Our approach provides a viable solution to achieve explainable automated
assessment in education. Code available at
https://github.com/lijiazheng99/aera.
