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A Human-in-the-Loop Framework for AI-Assisted Scoring in Large-Scale Writing Assessment

The paper introduces a human‑in‑the‑loop framework for AI‑assisted scoring of short written responses in a large‑scale national assessment. Using data from two recent test editions with about 5,000 responses each, the study validates that AI-generated scores align moderately to highly with human raters across multiple rubric dimensions. The framework also identifies when human review is most needed, allowing more efficient allocation of expert effort while maintaining assessment quality.

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arXiv AI
Sep 7

A Human-in-the-Loop Framework for AI-Assisted Scoring in Large-Scale Writing Assessment

The paper introduces a human‑in‑the‑loop framework for AI‑assisted scoring of short written responses in a large‑scale national assessment. Using data from two recent test editions with about 5,000 student responses each, the authors validate that AI‑generated scores align moderately to highly with human raters across multiple rubric dimensions. The framework includes a correction workflow that flags cases needing human review, thereby reducing manual workload while maintaining assessment quality.

By Mar\'ia Eugenia Curi, Germ\'an Capdehourat, Isabel Amigo, Magdalena Romano, Rosana Serra, Adri\'an Silveira, Andr\'es Peri
llmssafety
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arXiv AI
Jul 3

Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach

arXiv:2607. 02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation.

By Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira
llmsroboticssafety
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arXiv Machine Learning
4d ago

Limits of LLM Text Detectors in Education

The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.

By Lukas Gehring, Benjamin Paa{\ss}en
llmsbenchmarks
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