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Efektivitas Penerapan Media AI Presenter D-ID Dalam Meningkatkan Maharah Istima’ Siswa Ma Hasyim Asy’ari Jogoroto Jombang Bachrudin, Muhammad Ali; Rizki, Restu Budiansyah
EL-FUSHA: Jurnal Bahasa Arab dan Pendidikan Vol 6 No 2 (2025): EL-FUSHA : Jurnal Bahasa Arab dan Pendidikan
Publisher : Program Studi Pendidikan Bahasa Arab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/el-fusha.v6i2.10261

Abstract

In the current digital era, Arabic language teaching must evolve in line with advances in science and technology. In the field of education, the presence of AI offers significant potential to enhance the quality and effectiveness of learning, including in the cultivation of Maharah istima’ (listening skills). A primary challenge encountered by students encompasses both linguistic and non-linguistic factors, such as restricted vocabulary, difficulty in identifying phonetic sounds, issues with pronunciation and writing, a deficiency in concentration, and the insufficiency of suitable learning media. AI Presenter D-ID presents a potential solution to these problems. This study examines the effectiveness of AI Presenter D-ID in improving listening skills among tenth-grade students at MA Hasyim Asy'ari Jogoroto Jombang, using a quantitative approach with a one-group pretest-posttest pre-experimental design. The subjects of this study were all 27 students of class X-3 at MA Hasyim Asy’ari Jogoroto Jombang. Because the population size was under 100, the entire population was utilized as the research sample without the implementation of sampling techniques. Data were gathered through tests and documentation. The findings of the study demonstrate a significant enhancement in students' Maharah istima’ following the treatment. Based on the Wilcoxon Signed Rank Test, the result showed an Asymp. Sig. (2-tailed) value of 0.041<0.05. Consequently, the null hypothesis (H₀) is rejected and the alternative hypothesis (Hₐ) is accepted. This leads to the conclusion that the use of AI Presenter D-ID is effective in improving the Maharah istima’ of students in class X-3 at MA Hasyim Asy’ari Jogoroto Jombang. Keywords: Learning Media, AI Present er D-ID, Maharah Istima’.
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)
Publisher : Universitas Islam Negeri Walisongo Semarang - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/alsina.7.2.27500

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.