Developing a corpus-based paraphrase tool to improve EFL learners' writing skills
Abstract
Paraphrasing, or restating information using different words, is critical to successful writing. However, EFL learners have difficulty in making paraphrases to meet their writing demands, and there has been little research on developing automatic reference tools to assist these learners' paraphrasing skills for better writing quality. In this study, we developed PREFER, an online corpus-based paraphrasing assistance system. Allowing multi-word input and returning promptly with a list of paraphrases in English and Chinese, along with usage patterns and example sentences, PREFER provides substantial support for EFL learners to vary their expressions during writing. An assessment study of the effectiveness of PREFER was conducted with 55 Chinese-speaking EFL college freshmen in an Asian country. The results indicated that PREFER offered the most benefits to students' writing performance (with an after-use improvement of 38.2%), compared with an online dictionary and an online thesaurus (−31.6% and −6.2%, respectively). Further investigation revealed that the less proficient, more motivated, and more conservative students showed more significant progress in the paraphrasing task with the help of PREFER. In the meantime, nearly 90% of the students expressed satisfaction with the paraphrases generated by PREFER, and its functions, and another 75% of them acknowledged that PREFER benefits their writing task.
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There are legitimate uses of AI tools using LLMs in supporting the education of students.
“There are legitimate uses of these tools in supporting the education of students, meaning HEIs must carefully consider how policies dealing with student use of this software are created.”
From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Chen et al., 2015
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Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839 · see citing works
Moya, B., Eaton, S., Pethrick, H., Hayden, A., Brennan, R., Wiens, J., & McDermott, B. (2024) · Canadian Perspectives on Academic Integrity · doi:10.55016/ojs/cpai.v7i3.78123 · Cited by 48 · see citing works
EFL students’ perception of using AI paraphrasing tools in English language research projects
Alammar, A., & Abdel-Reheem Amin, E. (2023) · Arab World English Journal · doi:10.24093/awej/vol14no3.11 · Cited by 26 · see citing works
- Authors
- Chen, M.-H., Huang, S.-T., Chang, J. S., & Liou, H.-C.
- Affiliations
- National Tsing Hua University (TW)
- Corresponding author
- Chen, M.-H.
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2013-04-11
- Volume
- 28
- Issue
- 1
- Pages
- 22-40
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W2091550499
- Cited by
- 72
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 12 · 2022: 15 · 2021: 5 · 2020: 2 · 2019: 8 · 2018: 4 · 2017: 5 · 2015: 3 · 2014: 1 · 2013: 1
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Assisted Language Learning (publishedVersion)
- Funders
- National Science Council
- Academia Sinica
- National Tsing Hua University
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Keywords
- paraphrase, EFL writing, writing skills, student motivation, learner proficiency, student satisfaction
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