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

Authors

  • Restu Budiansyah Rizki UIN Maulana Malik Ibrahim Malang, 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 research aims to investigate the capabilities of Qalam AI – an automatic ḥarakat (diacritical mark) detection system – in supporting Arabic sentence learning through its ablility to analyze sentence components. This research was an exploratory study that employed a case study approach on the implementation of Qalam AI in the classroom. Data collection methods included: 1) analysis of student text samples, 2) expert evaluation by comparing Qalam AI’s analysis results with manual analysis by Arabic language experts, and 3) simulation of integration in the classroom. The analysis focused on three grammatical cases: 1) al-asmāʾ al-marfu̅ʿah (nominative), 2) al-asmāʾ al-manṣūbah (accusative), and 3) al-asmāʾ al-majrūrah (genitive). The research analysis used qualitative methods to examine Qalam AI's potential applications in Arabic language pedagogy.

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

Issue

Vol. 7 No. 2 (2025)

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