Systematic review: the application of ChatGPT on Arabic language text processing

Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.

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Publication Details

Journal
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Published
2025-09-09
DOI
https://doi.org/10.11591/ijece.v15i5.pp4837-4847
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Systematic review: the application of ChatGPT on Arabic language text processing

Ashanira Mat Deris, Fadzli Syed Abdullah, Ali Mousa AlSbou
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Artificial Intelligence in Healthcare and Education
article

Systematic review: the application of ChatGPT on Arabic language text processing

Ashanira Mat Deris, Fadzli Syed Abdullah, Ali Mousa AlSbou
article en

Abstract

Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringVol. 15(5)
Al-Hussein Bin Talal University (JO), Universiti Malaysia Terengganu (MY)
Universiti Malaysia Terengganu
Openalex Percentile: Top 82%
Artificial Intelligence in Healthcare and Education
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