Academic Journal
Analyzing Phonetic Errors Among Non-Native Arabic Learners Through Artificial Intelligence.
| Τίτλος: | Analyzing Phonetic Errors Among Non-Native Arabic Learners Through Artificial Intelligence. |
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| Συγγραφείς: | Nasution, Sahkholid, Asari, Hasan, Al-Rasyid, Harun, Khalilah, Zikrani |
| Πηγή: | Arabiyat: Journal of Arabic Education & Arabic Studies / Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban; Dec2025, Vol. 12 Issue 2, p229-242, 14p |
| Θεματικοί όροι: | Artificial intelligence, Automatic speech recognition, Phonetics, Arabic language, Computer assisted language instruction, Diagnostic examinations, Foreign language education |
| Abstract (English): | Accurate pronunciation is essential for non-native Arabic learners, yet many face persistent difficulties in articulating specific sounds, leading to phonetic errors that impede comprehension. With technological advancements, artificial intelligence (AI)— particularly Automatic Speech Recognition (ASR)—offers promising solutions for identifying and correcting such errors. This study explores the application of AI in analyzing phonetic inaccuracies among Arabic learners through an experimental model using ASR technology. Employing a descriptive-analytical approach, ten undergraduate students from Universitas Islam Negeri Sumatera Utara were asked to read short Arabic passages. Their recordings were processed through ASR to detect pronunciation errors, including sound substitution, weak articulation, and inaccuracies in elongation (madd) and nasalization (ghunnah). The results demonstrate that ASR effectively identifies recurring phonetic error patterns, providing instructors with valuable diagnostic insights and enabling timely, personalized feedback. The study concludes that integrating AI-driven pronunciation analysis into Arabic language instruction can significantly enhance learning outcomes, support autonomous practice, and promote more accurate oral proficiency. It recommends that educators and institutions adopt ASR-based tools as part of digital language pedagogy to strengthen pronunciation training and foster continuous learner engagement. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | المقال يركز على استخدام الذكاء الاصطناعي، وتحديداً تقنية التعرف التلقائي على الكلام، لتحليل الأخطاء الصوتية بين متعلمي اللغة العربية غير الناطقين بها. أُجري البحث مع عشرة طلاب دراسات جامعية من جامعة إسلام نيجيري سوماترا أوتارا في إندونيسيا، حيث استخدمت تقنية التعرف التلقائي على الكلام لتحديد الأخطاء الشائعة في النطق، مثل استبدال الأصوات وعدم الدقة في الإطالة والتنوين. تشير النتائج إلى أن هذه التقنية تكتشف بفعالية أنماط الأخطاء الصوتية المتكررة، مما يوفر ملاحظات قيمة لكل من المتعلمين والمدرسين، وتقترح أن دمج الأدوات المدفوعة بالذكاء الاصطناعي في تعليم اللغة العربية يمكن أن يعزز نتائج التعلم ويعزز الكفاءة الشفوية الأكثر دقة. يدعو البحث إلى اعتماد تقنية التعرف التلقائي على الكلام في البيئات التعليمية لدعم تدريب النطق وتعزيز تفاعل المتعلمين. [Extracted from the article] |
| Copyright of Arabiyat: Journal of Arabic Education & Arabic Studies / Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban is the property of Syarif Hidayatullah State Islamic University Jakarta and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Analyzing Phonetic Errors Among Non-Native Arabic Learners Through Artificial Intelligence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nasution%2C+Sahkholid%22">Nasution, Sahkholid</searchLink><br /><searchLink fieldCode="AR" term="%22Asari%2C+Hasan%22">Asari, Hasan</searchLink><br /><searchLink fieldCode="AR" term="%22Al-Rasyid%2C+Harun%22">Al-Rasyid, Harun</searchLink><br /><searchLink fieldCode="AR" term="%22Khalilah%2C+Zikrani%22">Khalilah, Zikrani</searchLink> – Name: TitleSource Label: Source Group: Src Data: Arabiyat: Journal of Arabic Education & Arabic Studies / Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban; Dec2025, Vol. 12 Issue 2, p229-242, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Phonetics%22">Phonetics</searchLink><br /><searchLink fieldCode="DE" term="%22Arabic+language%22">Arabic language</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+assisted+language+instruction%22">Computer assisted language instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+examinations%22">Diagnostic examinations</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+language+education%22">Foreign language education</searchLink> – Name: AbstractNonEng Label: Abstract (English) Group: Ab Data: Accurate pronunciation is essential for non-native Arabic learners, yet many face persistent difficulties in articulating specific sounds, leading to phonetic errors that impede comprehension. With technological advancements, artificial intelligence (AI)— particularly Automatic Speech Recognition (ASR)—offers promising solutions for identifying and correcting such errors. This study explores the application of AI in analyzing phonetic inaccuracies among Arabic learners through an experimental model using ASR technology. Employing a descriptive-analytical approach, ten undergraduate students from Universitas Islam Negeri Sumatera Utara were asked to read short Arabic passages. Their recordings were processed through ASR to detect pronunciation errors, including sound substitution, weak articulation, and inaccuracies in elongation (madd) and nasalization (ghunnah). The results demonstrate that ASR effectively identifies recurring phonetic error patterns, providing instructors with valuable diagnostic insights and enabling timely, personalized feedback. The study concludes that integrating AI-driven pronunciation analysis into Arabic language instruction can significantly enhance learning outcomes, support autonomous practice, and promote more accurate oral proficiency. It recommends that educators and institutions adopt ASR-based tools as part of digital language pedagogy to strengthen pronunciation training and foster continuous learner engagement. [ABSTRACT FROM AUTHOR] – Name: AbstractNonEng Label: Abstract (Arabic) Group: Ab Data: المقال يركز على استخدام الذكاء الاصطناعي، وتحديداً تقنية التعرف التلقائي على الكلام، لتحليل الأخطاء الصوتية بين متعلمي اللغة العربية غير الناطقين بها. أُجري البحث مع عشرة طلاب دراسات جامعية من جامعة إسلام نيجيري سوماترا أوتارا في إندونيسيا، حيث استخدمت تقنية التعرف التلقائي على الكلام لتحديد الأخطاء الشائعة في النطق، مثل استبدال الأصوات وعدم الدقة في الإطالة والتنوين. تشير النتائج إلى أن هذه التقنية تكتشف بفعالية أنماط الأخطاء الصوتية المتكررة، مما يوفر ملاحظات قيمة لكل من المتعلمين والمدرسين، وتقترح أن دمج الأدوات المدفوعة بالذكاء الاصطناعي في تعليم اللغة العربية يمكن أن يعزز نتائج التعلم ويعزز الكفاءة الشفوية الأكثر دقة. يدعو البحث إلى اعتماد تقنية التعرف التلقائي على الكلام في البيئات التعليمية لدعم تدريب النطق وتعزيز تفاعل المتعلمين. [Extracted from the article] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Arabiyat: Journal of Arabic Education & Arabic Studies / Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban is the property of Syarif Hidayatullah State Islamic University Jakarta and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.15408/a.v12i2.47342 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 229 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Phonetics Type: general – SubjectFull: Arabic language Type: general – SubjectFull: Computer assisted language instruction Type: general – SubjectFull: Diagnostic examinations Type: general – SubjectFull: Foreign language education Type: general Titles: – TitleFull: Analyzing Phonetic Errors Among Non-Native Arabic Learners Through Artificial Intelligence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nasution, Sahkholid – PersonEntity: Name: NameFull: Asari, Hasan – PersonEntity: Name: NameFull: Al-Rasyid, Harun – PersonEntity: Name: NameFull: Khalilah, Zikrani IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2356153X Numbering: – Type: volume Value: 12 – Type: issue Value: 2 Titles: – TitleFull: Arabiyat: Journal of Arabic Education & Arabic Studies / Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban Type: main |
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