# Calibrating Generative AI to Produce Realistic Essays for Data Augmentation

arXiv: https://arxiv.org/abs/2602.06772 · HTML: https://mlsanity.com/paper/2602.06772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores](https://mlsanity.com/paper/2203.00354/) (2022)
- [Data Augmentation for Automated Essay Scoring using Transformer Models](https://mlsanity.com/paper/2210.12809/) (2022)

## Abstract (arXiv)

Data augmentation can mitigate limited training data in machine-learning automated scoring engines for constructed response items. This study seeks to determine how well three approaches to large language model prompting produce essays that preserve the writing quality of the original essays and produce realistic text for augmenting ASE training datasets. We created simulated versions of student essays, and human raters assigned scores to them and rated the realism of the generated text. The results of the study indicate that the predict next prompting strategy produces the highest level of agreement between human raters regarding simulated essay scores, predict next and sentence strategies best preserve the rated quality of the original essay in the simulated essays, and predict next and 25 examples strategies produce the most realistic text as judged by human raters.