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

Rizki, Restu Budiansyah
Muhammad Fatkhur Rizal
Chusnia Rahmawati
Bachrudin, Muhammad Ali
Farhani, Siti
Batul, Zahadatul

Alsina : Journal of Arabic Studies
Vol. 7 No. 2 (2025)

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.