Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing
- URL: http://arxiv.org/abs/2505.17041v1
- Date: Tue, 13 May 2025 03:46:00 GMT
- Title: Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing
- Abstract summary: Generative Artificial Intelligence is transforming how English as a foreign language students write.<n>This study investigates how students integrate and modify AI-generated text when completing an expository writing task.
- Score: 2.8109476541924234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative Artificial Intelligence is transforming how English as a foreign language students write. Still, little is known about how students manipulate text generated by generative AI during the writing process. This study investigates how EFL secondary school students integrate and modify AI-generated text when completing an expository writing task. The study employed an exploratory mixed-methods design. Screen recordings were collected from 29 Hong Kong secondary school students who attended an AI-assisted writing workshop and recorded their screens while using generative AI to write an article. Content analysis with hierarchical coding and thematic analysis with a multiple case study approach were adopted to analyze the recordings. 15 types of AI-generated text edits across seven categories were identified from the recordings. Notably, AI-initiated edits from iOS and Google Docs emerged as unanticipated sources of AI-generated text. A thematic analysis revealed four patterns of students' editing behaviors based on planning and drafting direction: planning with top-down drafting and revising; top-down drafting and revising without planning; planning with bottom-up drafting and revising; and bottom-up drafting and revising without planning. Network graphs illustrate cases of each pattern, demonstrating that students' interactions with AI-generated text involve more complex cognitive processes than simple text insertion. The findings challenge assumptions about students' passive, simplistic use of generative AI tools and have implications for developing explicit instructional approaches to teaching AI-generated text editing strategies in the AFL writing pedagogy.
Related papers
- Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection [49.41415417307568]
OpAI-Bench is an operation-guided benchmark for studying progressive human-to-AI text transformation.<n>We show that AI-text detectability is governed not only by the proportion of AI-edited content, but also by edit operation, domain, and cumulative revision history.
arXiv Detail & Related papers (2026-06-04T17:58:05Z) - 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions [50.1119556361129]
We systematically define various notions of AI-generated text and their characteristics.<n>AITDNA is a new benchmark of human-machine co-constructed texts.<n>We benchmark various machine-generated text detectors and find that they often only perform well for specific notions but not as broad detectors.
arXiv Detail & Related papers (2026-06-03T14:05:14Z) - Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI [70.05655131235999]
Explainable Authorship Variational Autoencoder (EAVAE) is a novel framework that explicitly disentangles style from content.<n>Disentanglement is enforced through a novel discriminator.<n>EAVAE excels in few-shot learning over the M4 dataset for AI-generated text detection.
arXiv Detail & Related papers (2026-04-23T05:34:20Z) - Exploring how EFL students talk to and through AI to develop texts [15.134980087210025]
Generative Artificial Intelligence (AI) introduces new considerations for English as a foreign language (EFL) writing.<n>This study explores how students talk to and through AI by prompt engineering and negotiating.
arXiv Detail & Related papers (2026-04-06T04:51:05Z) - Writing With Machines and Peers: Designing for Critical Engagement with Generative AI [5.719812010814006]
This study proposes a pedagogical design that integrates AI and peer feedback in a graduate-level academic writing activity.<n>Students developed literature review projects through multiple writing and revision stages, receiving feedback from both a custom-built AI reviewer and human peers.
arXiv Detail & Related papers (2025-11-19T02:17:42Z) - A vibe coding learning design to enhance EFL students' talking to, through, and about AI [5.671810777243196]
This article reports on the piloting of vibe coding (using natural language to create software applications with AI) for English as a Foreign Language education.<n>We developed a human-AI meta-languaging framework with three dimensions: talking to AI (prompt engineering), talking through AI (negotiating authorship), and talking about AI.
arXiv Detail & Related papers (2025-09-09T00:27:04Z) - Can postgraduate translation students identify machine-generated text? [0.0]
This study explores the ability of linguistically trained individuals to discern machine-generated output from human-written text.<n>Twenty-three postgraduate translation students analysed excerpts of Italian prose and assigned likelihood scores to indicate whether they believed they were human-written or AI-generated.
arXiv Detail & Related papers (2025-04-12T09:58:09Z) - TextInVision: Text and Prompt Complexity Driven Visual Text Generation Benchmark [61.412934963260724]
Existing diffusion-based text-to-image models often struggle to accurately embed text within images.<n>We introduce TextInVision, a large-scale, text and prompt complexity driven benchmark to evaluate the ability of diffusion models to integrate visual text into images.
arXiv Detail & Related papers (2025-03-17T21:36:31Z) - Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners [3.2668433085737036]
Students can compose texts by integrating their own words with AI-generated text.<n>This study investigated how 59 Hong Kong secondary school students interacted with AI-generated text to compose a feature article.
arXiv Detail & Related papers (2025-03-01T06:29:00Z) - Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing [55.2480439325792]
This study systematically evaluations twelve state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset.<n>Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
arXiv Detail & Related papers (2025-02-21T18:45:37Z) - Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text [61.22649031769564]
We propose a novel framework, paraphrased text span detection (PTD)
PTD aims to identify paraphrased text spans within a text.
We construct a dedicated dataset, PASTED, for paraphrased text span detection.
arXiv Detail & Related papers (2024-05-21T11:22:27Z) - Purposeful remixing with generative AI: Constructing designer voice in multimodal composing [16.24460569356749]
This study investigates whether the use of generative AI tools could help student authors construct a more consistent voice in multimodal writing.
The study sheds light on the intentional and discursive nature of multimodal writing with AI as afforded by the technological flexibility.
arXiv Detail & Related papers (2024-03-28T02:15:03Z) - Raidar: geneRative AI Detection viA Rewriting [42.477151044325595]
Large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting.
We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output.
Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.
arXiv Detail & Related papers (2024-01-23T18:57:53Z) - Towards Possibilities & Impossibilities of AI-generated Text Detection:
A Survey [97.33926242130732]
Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses.
Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs.
To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
arXiv Detail & Related papers (2023-10-23T18:11:32Z) - Watermarking Conditional Text Generation for AI Detection: Unveiling
Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context.
Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
arXiv Detail & Related papers (2023-07-25T20:24:22Z) - FacTool: Factuality Detection in Generative AI -- A Tool Augmented
Framework for Multi-Task and Multi-Domain Scenarios [87.12753459582116]
A wider range of tasks now face an increasing risk of containing factual errors when handled by generative models.
We propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models.
arXiv Detail & Related papers (2023-07-25T14:20:51Z) - Exploring AI-Generated Text in Student Writing: How Does AI Help? [0.0]
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.
arXiv Detail & Related papers (2023-03-10T14:36:47Z)
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.