Cost-efficient generative AI summarization for scalable automated essay scoring in educational assessment
Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information loss when processing long essays. This study proposes a generative AI-assisted summarization framework to improve long-form essay representation while maintaining scoring reliability. Using the ASAP 2.0 dataset, we generate controlled-length summaries with three GPT-5 variants (GPT-5, GPT-5 mini, and GPT-5 nano) and use them as inputs for downstream AES models. To preserve original writing signals, handcrafted linguistic featu
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- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Cost-efficient generative AI summarization for scalable auto” ≈ “GoogleCloudPlatform/generative-ai””
- LinkedLinked via arxiv author · 85%Haowei Hua →
“Cost-efficient generative AI summarization for scalable automated essay scoring in educational assessment”
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Cost-efficient generative AI summarization for scalable auto” ≈ “steven2358/awesome-generative-ai””