Qalam AI: A Study on the Potential of Automatic Ḥarakat Detection for Arabic Sentence Learning

Authors

  • Restu Budiansyah Rizki Universitas Hasyim Asy’ari Jombang, Indonesia
  • Muhammad Fatkhur Rizal Universitas Hasyim Asy'ari, Indonesia
  • Chusnia Rahmawati Universitas Hasyim Asy'ari, Indonesia
  • Muhammad Ali Bachrudin Universitas Hasyim Asy’ari Jombang, Indonesia
  • Siti Farhani Universitas Hasyim Asy’ari Jombang, Indonesia
  • Zahadatul Batul Universitas Hasyim Asy’ari Jombang, Indonesia

DOI:

https://doi.org/10.21580/alsina.7.2.27500

Keywords:

Arabic NLP, Automatic Diacritization, Computer-Assisted Language Learning, Error Analysis, Natural Language Processing

Abstract

This study examines the linguistic performance and pedagogical relevance of Qalam AI as an automatic ḥarakāt detection system in Arabic sentence learning. Employing an exploratory qualitative case study design, the research involved analysis of student text samples, expert evaluation through comparison between AI-generated outputs and manual linguistic analysis, and classroom integration simulation. The analysis focused on three grammatical cases: al-asmāʾ al-marfūʿah (nominative), al-asmāʾ al-manṣūbah (accusative), and al-asmāʾ al-majrūrah (genitive). The findings indicate that Qalam AI is capable of identifying various sentence-level linguistic features, including grammatical case assignment, orthographic inconsistencies, sentence-structure variation, and punctuation-related issues, while also exhibiting systematic limitations in contexts involving morphological ambiguity and syntactic role differentiation. Rather than functioning as an error-free automation tool, Qalam AI appears to support form-focused learning by making linguistic features visible for learner reflection and instructional mediation. These findings suggest that Qalam AI may serve as a supportive pedagogical tool within AI-assisted Arabic language instruction, complementing human linguistic judgment rather than replacing it. The study contributes to ongoing discussions in Computer-Assisted Language Learning and Arabic Natural Language Processing by highlighting the instructional value of automatic diacritization systems beyond technical accuracy.

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Published

2025-08-31

How to Cite

Rizki, R. B., Muhammad Fatkhur Rizal, Chusnia Rahmawati, Bachrudin, M. A., Farhani, S., & Batul, Z. (2025). Qalam AI: A Study on the Potential of Automatic Ḥarakat Detection for Arabic Sentence Learning. Alsina : Journal of Arabic Studies, 7(2), 285–316. https://doi.org/10.21580/alsina.7.2.27500

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Vol. 7 No. 2 (2025)

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