Examining the effect of generative AI on students’ motivation and writing self-efficacy

The present study explores the effects of generative AI, specifically ChatGPT, in EFL classrooms on student motivation and writing efficacy. Motivation was measured through three components: Ideal L2 Self (IL2), Ought-to L2 Self (OL2), and L2 Learning Experience (L2LE). Participants (n = 327) were first and second-year undergraduate students at a Japanese university, enrolled in mandatory English classes focused on reading/writing or speaking/listening. The control group (n = 164) received peer feedback, whereas the treatment group (n = 163) utilized ChatGPT with specially crafted prompts for feedback. Both groups completed pre- and post-questionnaires to assess motivation and writing self-efficacy. Results affirmed that ChatGPT positively affected students’ motivation related to Ideal L2 Self and L2 Learning Experience. ChatGPT also significantly enhanced writing self-efficacy, which was found to correlate with all three motivational factors. However, there was no impact on Ought-to L2 Self motivation. The study highlights that ChatGPT’s integration can improve intrinsic motivation and writing self-efficacy, provided structured guidance is available to manage issues such as plagiarism. Future research should examine diverse samples, long-term effects, and ChatGPT’s impact on other language skills.

Authors

Institutions

Publication Details

Journal
Digital Applied Linguistics
Published
2024-11-28
DOI
https://doi.org/10.29140/dal.v1.102324
Citations
27
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
9.38
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Examining the effect of generative AI on students’ motivation and writing self-efficacy

Atsushi Mizumoto, Jerry Huang
27 citations
Digital Applied Linguistics
AI in Service Interactions
9.38
article

Examining the effect of generative AI on students’ motivation and writing self-efficacy

Atsushi Mizumoto, Jerry Huang
article en
27 citations

Abstract

The present study explores the effects of generative AI, specifically ChatGPT, in EFL classrooms on student motivation and writing efficacy. Motivation was measured through three components: Ideal L2 Self (IL2), Ought-to L2 Self (OL2), and L2 Learning Experience (L2LE). Participants (n = 327) were first and second-year undergraduate students at a Japanese university, enrolled in mandatory English classes focused on reading/writing or speaking/listening. The control group (n = 164) received peer feedback, whereas the treatment group (n = 163) utilized ChatGPT with specially crafted prompts for feedback. Both groups completed pre- and post-questionnaires to assess motivation and writing self-efficacy. Results affirmed that ChatGPT positively affected students’ motivation related to Ideal L2 Self and L2 Learning Experience. ChatGPT also significantly enhanced writing self-efficacy, which was found to correlate with all three motivational factors. However, there was no impact on Ought-to L2 Self motivation. The study highlights that ChatGPT’s integration can improve intrinsic motivation and writing self-efficacy, provided structured guidance is available to manage issues such as plagiarism. Future research should examine diverse samples, long-term effects, and ChatGPT’s impact on other language skills.

Digital Applied LinguisticsVol. 1
Kansai University (JP)
Quality Education
Openalex Percentile: Top 2%
AI in Service Interactions
9.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Examining the effect of generative AI on students’ motivation and writing self-efficacy — Atsushi Mizumoto, Jerry Huang · Digital Applied Linguistics (2024) | TGRS Research Map | TGRS