Distilling ChatGPT for Explainable Automated Student Answer Assessment

Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Keywords: Student answer assessment, Rationale generation, Large language model

Abstract: 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

Submission Number: 1034