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<p>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 (R<sup>2</sup> = 19.6%–46.4%). Our findings can enlighten innovative technological designs for automated writing evaluation tools and digitalized foreign language writing instruction.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Yupeng Lin,
  • Zhonggen Yu

摘要

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.