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Investigating the impacts of ChatGPT on students motivation in learning English grammar

Paper · 2025 · Florencia Cindy Gunawan and Allvian Ika Fiki Susanto

Summary

This small Indonesian study asks how English-major students feel ChatGPT affects their motivation to study grammar. It also asks how accurate they find its help and what makes it effective. The authors used a mixed-methods design. Twenty-five English majors from five universities in Central Java completed an online Likert questionnaire, and five of them were then interviewed. Questionnaire answers were summarised as mean scores for each sub-theme, and the interviews added qualitative explanation. Overall, students viewed ChatGPT as a motivating aid. They credited its instant corrections, simplified explanations, adjustable difficulty and support for self-study. Two concerns came up repeatedly: its feedback is sometimes wrong, and students may come to lean on it too heavily. Several participants preferred to combine it with conventional instruction. The authors admit that they never measured ChatGPT's actual accuracy or effectiveness and in effect treated both as given. This means the findings describe students' perceptions, not verified learning gains. The study matters mainly as early local evidence and as a reminder to use AI tutors critically.

Key points

  • The sample was 25 English-major students from 5 universities in Central Java, Indonesia, and 5 of them took part in follow-up interviews.
  • The instrument was a 19-item Google Forms questionnaire on a four-point Likert scale with no neutral option. It was adapted from Purnama et al. (2019), which builds on Clement (1994). The interview had six questions, all asked in English.
  • Analysis relied on average scores per sub-theme and did not use inferential statistics.
  • Students reported positive effects on motivation, efficiency and independent grammar study. Perceived weaknesses were occasional incorrect feedback, over-reliance, and trouble with idioms and context-dependent constructions.
  • The authors state that ChatGPT's real accuracy was not tested. They propose future work on measured accuracy, comparisons with other AI tools, and long-term effects across proficiency levels.

Interesting ideas

  • I1. The paper took a tool's accuracy on trust and only later flagged this as a gap, which shows that user satisfaction is no substitute for checking output against ground truth.
  • I2. A large survey was paired with a few interviews, so that aggregate scores could be explained by individual accounts.
  • I3. A four-point scale with no middle option makes every respondent take a side. This can sharpen judgments that would otherwise drift toward "unsure."
  • I4. Items were grouped into sub-themes and reported per theme rather than as one overall score, which exposes where a tool helps and where it does not.
  • I5. Students noticed that the model handled rule-based grammar well but stumbled on idioms and nuanced, context-dependent phrasing.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Every LLM teacher tier should be audited on a gold sample with negative controls, not trusted because its outputs look fluent.
  • I2 (learn): Alongside aggregate parser or QA metrics, a small random sample of errors could be reviewed by hand each round to explain why the numbers moved.
  • I3 (learn): When LLM teachers or witness parsers vote on silver data, a forced binary or ordinal verdict may give cleaner agreement signals than allowing abstention.
  • I4 (answer): School-test results could be broken down by skill, such as tense, coreference or arithmetic steps, so that weak spots are visible rather than averaged away.
  • I5 (frames): Idiomatic and non-compositional expressions could be detected and routed to dedicated handling instead of literal role assignment, with the decision recorded for explainability.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

Also on the byline: Universitas Nasional Karangturi.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Florencia Cindy Gunawan and Allvian Ika Fiki Susanto. 2025. Investigating the impacts of ChatGPT on students motivation in learning English grammar. Journal of English Language Teaching and Learning 6.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18860/jetle.v6i2.32397

@article{gunawan2025investigating,
  title = {{Investigating the impacts of ChatGPT on students motivation in learning English grammar}},
  author = {Florencia Cindy Gunawan and Allvian Ika Fiki Susanto},
  journal = {Journal of English Language Teaching and Learning 6},
  year = {2025},
  doi = {10.18860/jetle.v6i2.32397},
  url = {https://doi.org/10.18860/jetle.v6i2.32397}
}

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