Speaker
Description
The rapid advancement of Artificial Intelligence (AI) technologies has increased interest in their application within educational contexts. In particular, Short Answer Scoring (SAS), which focuses on the automated assessment of brief, descriptive answers, is attracting increasing attention. The primary reasons for researching SAS include reducing grading costs and facilitating real-time, interactive assessments in large-scale online courses (i.e., MOOCs). However, practical implementation faces two significant challenges: 1) ensuring the reliability of scoring results and 2) reducing the costs of building these models. In this talk, we tackle these two challenges. First, we introduce a practical human-in-the-loop framework for deploying automated SAS models to maintain scoring quality by having human experts re-grade low-reliable predictions generated by the model. We also propose 'cross-prompt training,' which can make the development of SAS models more cost-effective. Finally, considering these studies, we will discuss the potential for the practical application of automated SAS.
