Elucidating university students’ intentions to seek automated writing feedback from Grammarly: toward perceptual and systemic predictors

Automated writing evaluation has gained popularity in technology-enhanced language education, while learners’ technology acceptance of such tools has yet to be thoroughly investigated. Based on 487 university students’ perceptions, this structural equation modeling study finds that traditional hypotheses in the Unified Theory of Acceptance and Use of Technology (UTAUT) can be extended to explain higher education students’ Grammarly utilization. Students’ performance and effort expectancy significantly predict their behavioral intentions to use Grammarly. Facilitating conditions and behavioral intentions significantly predict actual use behavior. Additionally, students’ peer influence and trust in feedback significantly predict their performance expectancy. Peer influence, systemic interactivity, and personal investment significantly predict their effort expectancy. Students’ willingness for e-learning and instructional support significantly predict facilitating conditions, while peer influence does not. The proposed model yields moderate explanatory power for Grammarly acceptance and use among higher education students (R2 = 19.6%–46.4%). Our findings can enlighten innovative technological designs for automated writing evaluation tools and digitalized foreign language writing instruction.

Authors

Institutions

Publication Details

Journal
Humanities and Social Sciences Communications
Published
2025-08-18
DOI
https://doi.org/10.1057/s41599-024-03861-1
Citations
7
Primary Topic
Discourse Analysis in Language Studies
Type
article
Field-Weighted Citation Impact
106.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Elucidating university students’ intentions to seek automated writing feedback from Grammarly: toward perceptual and systemic predictors

Lin Yu-peng, Zhonggen Yu
7 citations
Humanities and Social Sciences Communications
Discourse Analysis in Language Studies
106.23
article

Elucidating university students’ intentions to seek automated writing feedback from Grammarly: toward perceptual and systemic predictors

Lin Yu-peng, Zhonggen Yu
article en
7 citations

Abstract

Automated writing evaluation has gained popularity in technology-enhanced language education, while learners’ technology acceptance of such tools has yet to be thoroughly investigated. Based on 487 university students’ perceptions, this structural equation modeling study finds that traditional hypotheses in the Unified Theory of Acceptance and Use of Technology (UTAUT) can be extended to explain higher education students’ Grammarly utilization. Students’ performance and effort expectancy significantly predict their behavioral intentions to use Grammarly. Facilitating conditions and behavioral intentions significantly predict actual use behavior. Additionally, students’ peer influence and trust in feedback significantly predict their performance expectancy. Peer influence, systemic interactivity, and personal investment significantly predict their effort expectancy. Students’ willingness for e-learning and instructional support significantly predict facilitating conditions, while peer influence does not. The proposed model yields moderate explanatory power for Grammarly acceptance and use among higher education students (R2 = 19.6%–46.4%). Our findings can enlighten innovative technological designs for automated writing evaluation tools and digitalized foreign language writing instruction.

Humanities and Social Sciences CommunicationsVol. 12(1)
Beijing Foreign Studies University (CN), Beijing Language and Culture University (CN)
Beijing Language and Culture University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 0%
Discourse Analysis in Language Studies
106.23
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.