# Exploring the effectiveness of ChatGPT-based feedback compared with teacher feedback and self-feedback: Evidence from Chinese to English translation

arXiv: https://arxiv.org/abs/2309.01645 · HTML: https://mlsanity.com/paper/2309.01645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 113 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Robust Neural Machine Translation with Doubly Adversarial Inputs](https://mlsanity.com/paper/1906.02443/) (2019)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [ChatGPT and Software Testing Education: Promises & Perils](https://mlsanity.com/paper/2302.03287/) (2023)
- [Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction](https://mlsanity.com/paper/2306.03090/) (2023)
- [Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods](https://mlsanity.com/paper/2105.03311/) (2021)
- [RECIPE: How to Integrate ChatGPT into EFL Writing Education](https://mlsanity.com/paper/2305.11583/) (2023)
- [Ethical ChatGPT: Concerns, Challenges, and Commandments](https://mlsanity.com/paper/2305.10646/) (2023)
- [Chinese Intermediate English Learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing](https://mlsanity.com/paper/2303.11812/) (2023)

## Built on by (most cited first, 1 of 1)

- [Integrating AI for Enhanced Feedback in Translation Revision- A Mixed-Methods Investigation of Student Engagement](https://mlsanity.com/paper/2410.08581/) (2024)

## Abstract (arXiv)

ChatGPT,a cutting-edge AI-powered Chatbot,can quickly generate responses on given commands. While it was reported that ChatGPT had the capacity to deliver useful feedback, it is still unclear about its effectiveness compared with conventional feedback approaches,such as teacher feedback (TF) and self-feedback (SF). To address this issue, this study compared the revised Chinese to English translation texts produced by Chinese Master of Translation and Interpretation (MTI) students,who learned English as a Second/Foreign Language (ESL/EFL), based on three feedback types (i.e., ChatGPT-based feedback, TF and SF). The data was analyzed using BLEU score to gauge the overall translation quality as well as Coh-Metrix to examine linguistic features across three dimensions: lexicon, syntax, and cohesion.The findings revealed that TF- and SF-guided translation texts surpassed those with ChatGPT-based feedback, as indicated by the BLEU score. In terms of linguistic features,ChatGPT-based feedback demonstrated superiority, particularly in enhancing lexical capability and referential cohesion in the translation texts. However, TF and SF proved more effective in developing syntax-related skills,as it addressed instances of incorrect usage of the passive voice. These diverse outcomes indicate ChatGPT's potential as a supplementary resource, complementing traditional teacher-led methods in translation practice.