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

• URL: http://arxiv.org/abs/2609.05143v1
• Date: Fri, 04 Sep 2026 13:47:29 GMT
• Title: A Human-in-the-Loop Framework for AI-Assisted Scoring in Large-Scale Writing Assessment
• Abstract summary: This study presents the design and validation of an AI-assisted scoring framework for written responses in a large-scale national assessment.<n>The proposed approach focuses on short written texts of approximately 150-200 words and incorporates a human-in-the-loop strategy to preserve assessment quality while reducing manual workload.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The integration of artificial intelligence (AI), particularly large language models (LLMs), into educational assessment has opened new opportunities to enhance the efficiency and scalability of grading processes. This study presents the design and validation of an AI-assisted scoring framework for written responses in a large-scale national assessment. The proposed approach focuses on short written texts of approximately 150-200 words and incorporates a human-in-the-loop strategy to preserve assessment quality while reducing manual workload. The study is grounded in a real operational context, using data from two recent editions of a nationwide test, each comprising approximately 5,000 student responses. We analyze the alignment between AI-generated scores and human raters across multiple rubric dimensions, as well as the impact of the proposed decision flow on pass/fail outcomes. Results show moderate to high agreement between the model and human evaluations in most dimensions, supporting the feasibility of AI assistance in this setting. Moreover, the proposed correction workflow identifies cases where human review is most valuable, enabling a more efficient allocation of expert effort. The findings suggest that AI-assisted scoring can be safely integrated into large-scale assessment processes only when combined with carefully designed human oversight. The paper concludes by discussing practical implications for deployment in national assessment systems and outlining future research directions, including longitudinal monitoring of model-human alignment and the analysis of potential cognitive bias introduced by AI-supported review workflows.

Related papers

• Beyond Accuracy: Towards a Robust Evaluation Methodology for AI Systems for Language Education [0.0]
  The rapid adoption of large language models in AI-powered language education has created an urgent need for evaluations that assess pedagogical effectiveness.<n>We introduce L2-Bench, a novel evaluation benchmark grounded in a validated "language learning experience designer" construct.<n>Our methodology integrates pedagogical theory, sociotechnical AI evaluation methods, and operationalizes a hierarchical taxonomy to structure an expert-curated dataset.
  arXiv  Detail & Related papers  (2026-03-20T16:13:03Z)
• From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making [0.5153774021264936]
  Empirical evidence shows that many failures arise from miscalibrated reliance.<n>This paper proposes a measurement framework for evaluating human-AI decision-making centered on team readiness.
  arXiv  Detail & Related papers  (2026-03-19T13:35:29Z)
• Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale [3.0317183198617066]
  We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise.<n>We propose a user study to evaluate the system's efficacy in improving research efficiency, inter-rater reliability, and researcher trust in AI-assisted qualitative analysis.
  arXiv  Detail & Related papers  (2026-03-09T14:01:42Z)
• Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback [81.0031690510116]
  We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages.<n>Our method is informed by a large scale analysis of human written novelty reviews.<n> Evaluated on 182 ICLR 2025 submissions, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions.
  arXiv  Detail & Related papers  (2025-08-14T16:18:37Z)
• Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education [3.557803321422781]
  This article presents a scalable, AI-supported framework for qualitative student feedback using large language models.<n>The system employs hierarchical summarization, anonymization, and exception handling to extract actionable themes from open-ended comments.<n>We report on its successful deployment across a large college of engineering.
  arXiv  Detail & Related papers  (2025-08-01T20:27:40Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning [63.531262595858]
  Divide-and-conquer approach breaks comprehensive evaluation task into localized scoring tasks, followed by a final global assessment.<n>We introduce a hybrid in-context learning approach that leverages human annotations to enhance the performance of both local and global evaluations.<n>Finally, we develop an uncertainty-based active learning algorithm that efficiently selects data samples for human annotation.
  arXiv  Detail & Related papers  (2025-05-26T16:39:41Z)
• STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond [68.47402386668846]
  We introduce Structured Reasoning In Critical Text Assessment (STRICTA) to model text assessment as an explicit, step-wise reasoning process.<n>STRICTA breaks down the assessment into a graph of interconnected reasoning steps drawing on causality theory.<n>We apply STRICTA to a dataset of over 4000 reasoning steps from roughly 40 biomedical experts on more than 20 papers.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• Position: AI Evaluation Should Learn from How We Test Humans [65.36614996495983]
  We argue that psychometrics, a theory originating in the 20th century for human assessment, could be a powerful solution to the challenges in today's AI evaluations.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.