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AI essay scoring gets structured frameworks for validity and cost

By PulseAugur Editorial · [2 sources] ·

Two new research papers propose structured frameworks for improving automated essay scoring (AES) using AI. The first paper introduces Evidence-Centered Design (ECD) to guide prompt engineering for large language models, aiming to enhance the validity, reliability, and fairness of AI-generated educational outputs. The second paper presents a Cost-Aware Measurement Design (CAMD) Framework, which reframes AES cost as a measurement design concern rather than a purely technical efficiency issue, focusing on minimizing the cost per defensible score. AI

IMPACT These frameworks aim to improve the rigor and cost-effectiveness of AI in educational assessment, potentially leading to more reliable and fair automated scoring systems.

RANK_REASON Two academic papers published on arXiv proposing new frameworks for AI applications.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI essay scoring gets structured frameworks for validity and cost

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiawei Xiong, Cheng Tang, Hong Jiao, Hanna Choi ·

    Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring

    arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks such as item generation, automated scoring, and feedback delivery. The quality of …

  2. arXiv cs.CL TIER_1 English(EN) · Yi Gui ·

    From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring

    arXiv:2610.19287v1 Announce Type: cross Abstract: Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern. Yet deployment decisions require more than reporting latency, model size, or inference expe…

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