# Exploring AI-Generated Text in Student Writing: How Does AI Help?

arXiv: https://arxiv.org/abs/2304.02478 · HTML: https://mlsanity.com/paper/2304.02478/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum](https://mlsanity.com/paper/2101.07570/) (2021)

## Built on by (most cited first, 4 of 4)

- [Distributed agency in second language learning and teaching through generative AI](https://mlsanity.com/paper/2403.20216/) (2024)
- [Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing](https://mlsanity.com/paper/2505.17041/) (2025)
- [Product vs. Process: Exploring EFL Students' Editing of AI-Generated Text for Expository Writing](https://mlsanity.com/paper/2507.21073/) (2025)
- [The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings](https://mlsanity.com/paper/2604.15460/) (2026)

## Abstract (arXiv)

English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools.