# AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy

arXiv: https://arxiv.org/abs/2512.04113 · HTML: https://mlsanity.com/paper/2512.04113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Towards LLM-based Autograding for Short Textual Answers](https://mlsanity.com/paper/2309.11508/) (2023)
- [Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference](https://mlsanity.com/paper/1809.01357/) (2018)
- [Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading](https://mlsanity.com/paper/2309.09338/) (2023)
- [Automatic Short Answer Grading via Multiway Attention Networks](https://mlsanity.com/paper/1909.10166/) (2019)
- [Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model](https://mlsanity.com/paper/2305.18638/) (2023)
- [Grading Massive Open Online Courses Using Large Language Models](https://mlsanity.com/paper/2406.11102/) (2024)
- [Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading](https://mlsanity.com/paper/2009.01303/) (2020)
- [Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset](https://mlsanity.com/paper/2310.18373/) (2023)
- [The Hidden AI Race: Tracking Environmental Costs of Innovation](https://mlsanity.com/paper/2511.22781/) (2025)

## Abstract (arXiv)

Constructed-response questions are crucial to encourage generative processing and test a learner's understanding of core concepts. However, the limited availability of instructor time, large class sizes, and other resource constraints pose significant challenges in providing timely and detailed evaluation, which is crucial for a holistic educational experience. In addition, providing timely and frequent assessments is challenging since manual grading is labor intensive, and automated grading is complex to generalize to every possible response scenario. This paper proposes a novel and practical approach to grade short-answer constructed-response questions. We discuss why this problem is challenging, define the nature of questions on which our method works, and finally propose a framework that instructors can use to evaluate their students' open-responses, utilizing near-domain data like data from similar questions administered in previous years. The proposed method outperforms the state of the art machine learning models as well as non-fine-tuned large language models like GPT 3.5, GPT 4, and GPT 4o by a considerable margin of over 10-20% in some cases, even after providing the LLMs with reference/model answers. Our framework does not require pre-written grading rubrics and is designed explicitly with practical classroom settings in mind. Our results also reveal exciting insights about learning from near-domain data, including what we term as accuracy and data advantages using human-labeled data, and we believe this is the first work to formalize the problem of automated short answer grading based on the near-domain data.