---
title: "AI-Assisted Learning in Arabic Grammar: Investigating the Use of"
description: "The study looks at how university students of Arabic grammar and translation (Nahwu wa Tarjamah, centred on i‘rāb, the… 29 references."
authors: ["Rani Ismil Hakim", "Isra Hayati  Darman", "Ade Destri  Deviana", "Lailatul Qomariyah", "Usfiyatur  Rusuly", "Elvina Gusman"]
year: 2026
venue: "Al-Ta'rib 14"
source: "https://e-journal.iain-palangkaraya.ac.id/tarib/article/view/11513"
doi: "10.23971/altarib.v14i1.11513"
licence: "CC BY-SA (journal licence per DOAJ; Al-Ta'rib)"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hakim-2026-ai-assisted-learning-arabic-grammar/"
---
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# AI-Assisted Learning in Arabic Grammar: Investigating the Use of ChatGPT and Digital Platforms in Arabic Learning

**Authors:** Rani Ismil Hakim, Isra Hayati  Darman, Ade Destri  Deviana, Lailatul Qomariyah, Usfiyatur  Rusuly and Elvina Gusman · **Year:** 2026 · **Venue:** [Al-Ta'rib 14](https://md.research.elamur.ai/free/en/venues/al-ta-rib-jurnal-ilmiah-program-studi-pendidikan-bahasa-arab-iain-palangka-raya/) ([Al-Ta rib Jurnal Ilmiah Program Studi Pendidikan Bahasa Arab IAIN Palangka Raya 2026](https://md.research.elamur.ai/free/en/venues/al-ta-rib-jurnal-ilmiah-program-studi-pendidikan-bahasa-arab-iain-palangka-raya/2026/)) · **Publisher:** [LP2M IAIN Palangka Raya](https://md.research.elamur.ai/free/en/publishers/lp2m-iain-palangka-raya/) · **Licence:** CC BY-SA (journal licence per DOAJ; Al-Ta'rib)

**Links:** [Publisher's page](https://doi.org/10.23971/altarib.v14i1.11513) · [PDF](https://jurnal.uin-palangkaraya.ac.id/tarib/article/download/11513/3478) · [DOI 10.23971/altarib.v14i1.11513](https://doi.org/10.23971/altarib.v14i1.11513) · [OpenAlex](https://openalex.org/W7164677802)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The study looks at how university students of Arabic grammar and translation (Nahwu wa Tarjamah, centred on i‘rāb, the grammatical parsing of each word) use AI tools, and whether that use helps their learning. Research so far has mostly measured how accurate such systems are, not how learners use them. The authors ran a qualitative case study, backed by descriptive numbers, in a fifth-semester Nahwu III course at STAIN Mandailing Natal, Indonesia, with 30 students over one semester (2025–2026). The lecturer first showed how to use the tools responsibly and asked students to check AI answers against classical grammar references. Data came from classroom observation, an end-of-term questionnaire, semi-structured interviews and grade records, analysed with the Miles and Huberman interactive model. Students relied on four kinds of resources, and the authors report higher motivation, more independent study, and a mean final score of 88.2. The authors do not claim AI caused these results. Their point is that AI acts as scaffolding only when a teacher guides it, because it still fails on harder syntax.

## Key points
- Participants: 30 undergraduates in Arabic Language Education, from both pesantren and general secondary schools, chosen by purposive sampling.
- Tools introduced: students were shown Gemini, ChatGPT and NahwuAI, along with Tashkeel and an Arabic Keyboard, plus online libraries of Islamic texts, web-based dictionaries and sites that explain Arabic grammar.
- The tools fall into four groups: digital repositories, generative AI, machine translation, and grammar-support sites.
- The questionnaire had seven categories, including frequency of use, perceived usefulness, use for i‘rāb, willingness to recommend and preferred platforms.
- Grades: 17 students got A, 11 got B, 2 got C and none got D or E, for a mean of 88.2.
- Known AI weak spots were ḥadhf (ellipsis) and taqdīm wa ta'khīr (word-order variation).

## Interesting ideas
- AI output is treated as a draft that must be checked against an authoritative grammar before it counts as correct, which keeps a cheap generator from replacing trusted ground truth.
- Mistakes cluster in two predictable places, ellipsis and moved constituents, so trust in a model can depend on the type of construction.
- The authors refuse to read high grades as proof that AI works, because there was no comparison group. This is a useful habit for any evaluation without baselines.
- Each answer is backed by an analysis that names every word's grammatical role, so the reasoning can be checked and not just the final result.
- The tool is most helpful as temporary support for tasks just beyond the learner's level, with a supervisor catching errors.

## For Mova

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

- **I1** (learn): LLM-teacher parses could become silver data only after they agree with treebank-backed witness parsers or grammar rules, never on the teacher's word alone.
- **I2** (tree): build focused English test sets for gapping, ellipsis and fronting/topicalization, and lower confidence in LLM-teacher trees on those patterns.
- **I3** (learn): every reported gain should be paired with a negative control, such as the same pipeline without the new component, before it is credited.
- **I4** (answer): student readers could show the word-by-word role analysis behind each test answer, so wrong answers can be traced to a specific parsing or role error.
- **I5** (learn): self-play or curriculum training could feed in harder constructions step by step, with a stronger checker reviewing outputs on those harder items.

*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

- [Universitas Islam Negeri Maulana Malik Ibrahim](https://md.research.elamur.ai/free/en/universities/universitas-islam-negeri-maulana-malik-ibrahim/) — University, Malang, Indonesia · 4 works
- [Sekolah Tinggi Agama Islam Negeri Mandailing Natal](https://md.research.elamur.ai/free/en/universities/sekolah-tinggi-agama-islam-negeri-mandailing-natal/) — University, Panyambungan, Indonesia · 2 works
- [Universitas Hasyim Asy'ari](https://md.research.elamur.ai/free/en/universities/universitas-hasyim-asy-ari/) — University, Jombang, Indonesia · 2 works

Also on the byline: [Central Queensland University](https://ror.org/023q4bk22).

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🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works

## Cite

Cite as: Rani Ismil Hakim, Isra Hayati  Darman, Ade Destri  Deviana, Lailatul Qomariyah, Usfiyatur  Rusuly and Elvina Gusman. 2026. AI-Assisted Learning in Arabic Grammar: Investigating the Use of ChatGPT and Digital Platforms in Arabic Learning. Al-Ta'rib 14.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hakim2026ai,
  title = {{AI-Assisted Learning in Arabic Grammar: Investigating the Use of ChatGPT and Digital Platforms in Arabic Learning}},
  author = {Rani Ismil Hakim, Isra Hayati Darman, Ade Destri Deviana, Lailatul Qomariyah, Usfiyatur Rusuly and Elvina Gusman},
  booktitle = {Al-Ta'rib 14},
  year = {2026},
  doi = {10.23971/altarib.v14i1.11513},
  url = {https://doi.org/10.23971/altarib.v14i1.11513}
}
```

- [← previous AI systems and the reproduction of (standard) language ideologies in World Englishes](https://md.research.elamur.ai/free/en/papers/ugwuanyi-2026-ai-systems-reproduction-standard-language/)
- [next → AI-TraLow: AI-Driven Translation for Low-Resource Languages and Cultures](https://md.research.elamur.ai/free/en/papers/oliver-2026-ai-tralow-ai-driven-translation/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/21/)

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*Updated 2 November 2026*