asap-2-0-f85c304a·1 events·first seen Aliases: ASAP 2.0
Researchers propose a hybrid framework for automated essay scoring (AES) that uses GPT-5, GPT-5 mini, and GPT-5 nano to generate controlled-length summaries of long essays, addressing transformer input-length limitations. Summaries are combined with handcrafted linguistic features and fed into downstream AES models evaluated on the ASAP 2.0 dataset using quadratic weighted kappa. GPT-5 mini achieves the best human-rating agreement while GPT-5 produces higher summarization quality, revealing cost-performance trade-offs. The study also finds that higher-scoring, more complex essays are harder to compress without information loss, raising fairness concerns for educational deployment.