The emergence of generative artificial intelligence (Generative AI), such as ChatGPT, has brought significant changes to higher education, including Arabic language learning for non-native speakers (Arabic as a Foreign Language). Writing skills (Maharah al-Kitabah) are considered a relatively complex productive skill. Learners are required not only to master word formation (Sarf) and Arabic grammar (Nahwu), but also to organize ideas coherently and logically. This article employs a Systematic Literature Review (SLR) approach based on the PRISMA 2020 standards to map the use of ChatGPT in Arabic writing, examine the reliability of AI in Arabic grammar, and identify research gaps that remain underexplored. The review findings indicate that ChatGPT can serve as a useful writing partner (human-AI co-writer), particularly for generating ideas, enriching vocabulary, and correcting basic grammatical errors. However, AI still frequently produces errors or “hallucinations” when dealing with morphologically complex words, variations in Arabic dialects, and unvocalized texts. As a result, AI-generated writing may appear linguistically polished on the surface while containing grammatical inaccuracies. Furthermore, excessive reliance on AI may pose risks to academic integrity, while AI detection tools themselves often produce inaccurate results when identifying Arabic-language texts (false positives). This review also finds that existing research remains overly focused on English, tends to examine only the final writing outcomes without investigating students’ cognitive processes, and has paid limited attention to AI models specifically developed for the Arab region, such as ALLaM and Jais. Therefore, the approach to AI-assisted learning needs to shift from merely using AI toward purposeful and reflective utilization through frameworks such as the Spiral Learning Model and Genre-Based Pedagogy, so that AI functions as a scaffold without diminishing students’ autonomy and critical thinking.
This study aims to analyze language errors and problems in the presentation of materials in the latest edition of Al Muyassar Fii ‘Ilmin Nahwi, Volume 2. This study is based on the importance of accuracy in Arabic grammar textbooks and previous findings of errors in Volume 1, which show the need to evaluate the following volume. This research uses a descriptive qualitative approach with content analysis. The data were collected by examining the texts, examples, tables, and exercises in Volume 2 of Al Muyassar. The data were then classified according to the types of errors and compared with the previous edition to identify patterns and changes. The results show that syntactic errors were the most common, accounting for 34% of the findings. Spelling (imlā’) and morphological errors each accounted for 19%, semantic errors accounted for 8%, and other types of errors accounted for 20%. In addition to language errors, the study also found problems related to the completeness of the materials, the classification of examples, the accuracy of quotations from the Qur’an and hadith, and the presentation of the content. A comparison with the previous edition showed that approximately 57% of the findings in the latest edition were not found in the earlier version. This indicates the need for stricter checks during text reproduction, editing, and digitization. These findings suggest that each volume of Al Muyassar needs to be evaluated separately because the problems found in each volume are not entirely the same. This study recommends a multi-stage editing and verification process covering syntax, morphology, spelling, semantics, material presentation, and source quotations to ensure that Volume 2 is more accurate and suitable for use as an Arabic grammar textbook.
The landscape of foreign language education, particularly Arabic language instruction, is currently undergoing a fundamental transformation driven by the emergence of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs). This Systematic Literature Review evaluates recent developments in the integration of GenAI into the Arabic language learning ecosystem, encompassing trends in the evolution of language models, pedagogical effectiveness, the utilization of automated assessment systems, as well as the linguistic and cultural challenges involved. Through a thematic synthesis of empirical and theoretical literature published between 2019 and 2026, this review confirms that GenAI significantly enhances learner engagement, instructional personalization, and language skill acquisition, particularly in vocabulary mastery and reading comprehension. These improvements are driven by the adoption of general-purpose models such as ChatGPT, as well as leading Arabic-centric models such as JAIS and ALLaM. Nevertheless, this innovative integration encounters the inherent structural complexity of the Arabic language. Generative models frequently experience technical limitations in handling syntactic rules (nahwu), morphology (shorof), diacritic disambiguation, and regional dialect variations, as evidenced by performance gaps in linguistic benchmarks such as AraLingBench and AlGhafa. Furthermore, this review discusses rapid advances in trait-based Automated Essay Scoring (AES) supported by precise prompt engineering. Based on these findings, the study concludes that although GenAI has significant potential to bring positive disruption, its successful implementation depends heavily on pedagogical readiness, digital literacy, the application of Human-in-the-Loop (HITL) collaboration, and the development of ethical frameworks aligned with the epistemology of Islamic higher education.
Assessment in Arabic language learning as a foreign language (Arabic as a Foreign Language) is currently undergoing a significant paradigm shift. This transformation is influenced by the development of digital technology, the utilization of Artificial Intelligence (AI), and the implementation of the Merdeka Curriculum in Indonesia, which emphasizes flexibility, differentiation, and learning personalization. This article presents a Systematic Literature Review (SLR) aimed at identifying, examining, and synthesizing developments, innovations, methodological approaches, and various challenges in the assessment of the four Arabic language skills, namely listening, speaking, reading, and writing, during the period from 2020 to early 2026. The review employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to identify and analyze relevant literature. The findings indicate a shift from conventional evaluation systems based on discrete tests toward the implementation of Technology-Enhanced Assessment (TEA) and more authentic forms of assessment. The literature synthesis reveals several key findings. First, diagnostic assessment, encompassing both cognitive and non-cognitive aspects, plays an important role in designing differentiated learning within the framework of the Merdeka Curriculum. Second, the development of assessment instruments for Arabic language maharah is increasingly oriented toward measuring higher-order thinking skills (Higher Order Thinking Skills/HOTS). Third, the utilization of Generative AI, including ChatGPT, as well as various gamification-based learning platforms, has introduced new approaches to providing faster, more adaptive, and interactive feedback. Based on the synthesis, the effectiveness of modern assessment is not determined solely by the sophistication of the technology employed, but also by the alignment between technology and pedagogical objectives, the transparency of assessment procedures, and its ability to comprehensively represent learners' communicative competence while maintaining the principles of academic integrity.
