Differentiated Performance Study of Teachers and AI in Second Language Writing Feedback

DOI

10.2991/978-2-38476-523-2_28How to use a DOI?

Keywords

teacher feedback; AI feedback; second language writing; difference

Abstract

This study investigates the comparative effects of teacher feedback and AI-assisted feedback in second language (L2) writing instruction. Acknowledging the challenges teachers encounter in delivering comprehensive and timely feedback, this research focuses on the emergence and potential of Natural Language Processing (NLP) and Machine Learning (ML)-powered AI tools, aiming to assess how they can address the shortcomings of existing feedback mechanisms.

An empirical study was conducted involving 38 English major students over the course of a semester. These students were divided into two groups: a teacher feedback group and an AI feedback group, allowing for a comparative analysis of the two feedback models.

The study revealed that the AI feedback group achieved significantly higher writing scores in the post-test. AI feedback demonstrated unique advantages in terms of immediacy, accessibility, objectivity, and computational efficiency in handling large volumes of work. It particularly excelled in correcting low-level errors such as grammar and spelling by leveraging sophisticated algorithms for syntactic and morphological analysis, and also helped alleviate students’ psychological pressure. However, the research also emphasized that despite AI’s excellent performance in mechanical corrections, human teachers remain irreplaceable in understanding complex semantics, providing higher-order thinking guidance, and offering emotional support. Consequently, the study advocates for a hybrid feedback model of human-machine collaboration, where AI functions as an intelligent automated system (often integrated within Learning Management Systems - LMS) handling fundamental correction tasks, thereby allowing teachers to focus on fostering students’ creativity, deep thinking, and individualized guidance. This approach aims to jointly construct a more efficient and comprehensive future for L2 writing instruction.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wende Zhou
PY  - 2025
DA  - 2025/12/29
TI  - Differentiated Performance Study of Teachers and AI in Second Language Writing Feedback
BT  - Proceedings of the 5th International Conference on New Media Development and Modernised Education (NMDME 2025)
PB  - Atlantis Press
SP  - 268
EP  - 279
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-523-2_28
DO  - 10.2991/978-2-38476-523-2_28
ID  - Zhou2025
ER  -