This study examines input optimization for enhanced efficiency in automated scoring (AS) of reading assessments, which typically involve lengthy passages and complex scoring guides. We propose optimizing input size using question-specific summaries and simplified scoring guides. Findings indicate that input optimization via compression is achievable while maintaining AS performance.

This study proposes an innovative method for evaluating cross-country scoring reliability (CCSR) in multilingual assessments, using hyperparameter optimization and a similarity-based weighted majority scoring within a single human scoring framework. Results show that this approach provides a cost-effective and comprehensive assessment of CCSR without the need for additional raters.