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

arXiv: https://arxiv.org/abs/2609.05143 · HTML: https://mlsanity.com/paper/2609.05143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [A Survey of Uncertainty Estimation Methods on Large Language Models](https://mlsanity.com/paper/2503.00172/) (2025)
- [Item Response Theory -- A Statistical Framework for Educational and Psychological Measurement](https://mlsanity.com/paper/2108.08604/) (2021)
- [LLM-based Automated Grading with Human-in-the-Loop](https://mlsanity.com/paper/2504.05239/) (2025)
- [Automated Long Answer Grading with RiceChem Dataset](https://mlsanity.com/paper/2404.14316/) (2024)
- [Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams](https://mlsanity.com/paper/2411.05231/) (2024)
- [Rubric-Conditioned LLM Grading: Alignment, Uncertainty, and Robustness](https://mlsanity.com/paper/2601.08843/) (2025)
- [Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence](https://mlsanity.com/paper/2509.10591/) (2025)
- [On the effectiveness of LLMs for automatic grading of open-ended questions in Spanish](https://mlsanity.com/paper/2503.18072/) (2025)
- [Towards Transparent AI Grading: Semantic Entropy as a Signal for Human-AI Disagreement](https://mlsanity.com/paper/2508.04105/) (2025)
- [How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment](https://mlsanity.com/paper/2602.16039/) (2026)

## Abstract (arXiv)

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.