Alibbaa': Jurnal Pendidikan Bahasa Arab published twice a year (January and July), is a multilingual (Bahasa, Arabic, and English), peer-reviewed journal, and specializes in Arabic Education and Arabic Studies. This journal is published by the Arabic Language Education Department, Tarbiyah Faculty and Teacher Training, Universitas Islam Negeri Madura. Editors welcome scholars, researchers and practitioners of Arabic Education around the world to submit scholarly articles to be published through this journal. All articles will be reviewed by experts before accepted for publication. Each author is solely responsible for the content of published articles. P-ISSN: 2721-1606 E-ISSN: 2716-4985 AUTHOR GUIDELINE Author Guideline is available here Journal Template is available here It is suggested the use of a reference manager, MENDELEY and ZOTERO
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A Conceptual Framework of Arabic Reading Skills in Qur’anic Memorization: Integrating Philosophy of Science and AI-Assisted Learning
This study addresses the tendency in Qur’anic learning research to separate reading skills (maharah qira’ah) and memorization, without integrating them within a philosophy of science framework. In addition, the use of Artificial Intelligence (AI) remains largely technical. This study aims to construct a conceptual framework integrating reading and memorization from ontological, epistemological, and axiological perspectives, and to explore AI-assisted learning as a supporting approach. A qualitative systematic literature-based conceptual design was employed using thematic analysis and conceptual synthesis. The findings indicate that reading functions ontologically as a meaning-making process, epistemologically as a mechanism of knowledge construction through encoding, storage, and retrieval, and axiologically as a means to improve memorization quality in terms of accuracy, retention, and efficiency. AI integration strengthens these processes through real-time feedback and adaptive learning, enabling an integrated learning cycle between reading and memorization. This study contributes a conceptual framework linking reading, memorization, and technology within a unified epistemic system
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Rethinking AI in Arabic Translation Learning: The Role of DeepL and Teacher Mediation
This study aims to analyze the implementation of DeepL AI Translate in Arabic translation learning at MTs Alkhairaat Pusat Palu Indonesia, examine its impact on students’ learning processes, and identify the role of teachers in controlling the quality of AI-assisted translations. This research employs a qualitative descriptive approach within a naturalistic framework, with data collected through 15 days of classroom observations, in-depth interviews, and documentation. The findings reveal that DeepL is integrated into a structured learning process consisting of pre-translation, AI-assisted translation, post-translation, and evaluation stages. The use of DeepL enhances student participation, learning efficiency, confidence, and vocabulary comprehension. However, its use remains largely limited to lexical-level support and may lead to dependency if not pedagogically controlled. In this context, teachers play a crucial role as mediators, facilitators, and quality controllers of translation outcomes. This study highlights that the effectiveness of AI in language learning depends not only on technological capability but also on its pedagogical integration within the classroom.
Learning Arabic Speaking (Kalam) through Thorndike’s Behaviorism: Pedagogical Implications for AI-Assisted Learning
This study investigates the implementation of Arabic speaking instruction in Thorndike’s behaviorism principles (law of readiness, law of exercise, and law of effect) at SMPIT Imam An-Nawawi Pekanbaru and explores their pedagogical implications for AI-assisted language learning. Using a qualitative case study design, the research involved classroom observations, semi-structured interviews, and documentation in Arabic speaking classes. The findings reveal that law of readiness was reflected through verbal stimulation and guided prompting that prepared students’ speaking engagement, while law of exercise appeared through repetition, drilling, and repeated speaking interaction that gradually formed students’ speaking habits. In addition, law of effect was implemented through positive reinforcement, corrective feedback, and motivational responses that strengthened students’ speaking confidence and verbal participation. The study also found that pedagogical mechanisms identified in the classroom, such as repetition, adaptive prompting, corrective feedback, and reinforcement-based interaction, conceptually align with the learning logic used in contemporary AI-assisted language learning systems. The study concludes that Thorndike’s behaviorism remains pedagogically relevant in Arabic speaking instruction and offers conceptual insights for the development of AI-assisted Arabic language learning, particularly in speaking habit formation and adaptive verbal interaction.
Local Dialect Interference and AI-Based Arabic Speech Recognition: Evidence from Madurese Learners
This study investigates the influence of Madurese phonetic interference on the accuracy of AI-based Arabic Speech Recognition systems. The research focuses on vowel-shift deviations produced by Madurese native speakers in Arabic pronunciation and evaluates the performance of Google Speech-to-Text and OpenAI Whisper in recognizing dialect-influenced Arabic speech. Using a mixed-method approach, speech data were collected from 13 sixth-semester students of the Arabic Language Education Department at UIN Madura through direct voice recording involving isolated words, continuous Arabic sentences, and Qur’anic recitations. Acoustic observation using Praat identified systematic vowel shifts, particularly the transitions of /a/ to [e], /i/ to [e], and /u/ to [o], which generated acoustic variability in Arabic speech production. The findings reveal that OpenAI Whisper achieved higher recognition accuracy (84.0%) with a lower Word Error Rate (16.0%) compared to Google Speech-to-Text, which obtained an accuracy of 61.8% and a WER of 38.2%. The dominant errors included substitution, deletion, insertion, and segmentation errors, indicating instability in phoneme mapping caused by dialect-induced vowel transitions. The study concludes that current AI-based Arabic Speech Recognition systems remain sensitive to low-resource dialect interference and that acoustic variability significantly affects recognition stability in multilingual speech environments. These findings highlight the importance of developing more adaptive and linguistically inclusive ASR models for Arabic language learning contexts.
Bridging Traditional Arabic Pedagogy and AI-Supported Learning through Snakes and Ladders Game-Based Instruction
This study explores the use of snakes and ladders game-based instruction in Arabic uslub learning at LSBA Ula within the broader perspective of AI-supported pedagogy. The study emerged from the challenges of contextual Arabic language learning, particularly students’ difficulties in understanding the communicative functions and contextual usage of Arabic expressions through conventional teacher-centered instruction. Using a qualitative case study approach, data were collected through classroom observation, semi-structured interviews, and learning documentation involving students and teachers participating in Arabic uslub instruction. The findings reveal that the traditional snakes and ladders game created collaborative and contextual learning interactions through peer discussion, meaning negotiation, reflective feedback, and experiential learning activities. The study also found that the pedagogical mechanisms emerging during gameplay, including real-time feedback, error identification, and adaptive instructional support, align with the principles of AI-supported learning without transforming the traditional game into a digital AI-based system. This study concludes that traditional game-based instruction remains pedagogically relevant for Arabic language learning in the AI era because it supports collaborative, reflective, and human-centered learning experiences. The study contributes to the development of low-tech yet AI-compatible Arabic pedagogy, particularly in non-formal educational contexts.
Integrating the Flower and Hayes Cognitive Writing Model into Advanced Arabic Dictation Instruction with AI-Assisted Learning
This study examines the relevance of the cognitive writing model developed by Linda Flower and John R. Hayes in advanced Arabic dictation instruction and explores the integration of Artificial Intelligence (AI) in supporting cognitive writing processes during orthographic learning. The study is based on the assumption that Arabic dictation (imla’) is not merely a mechanical orthographic exercise, but a cognitive writing activity involving planning, linguistic processing, monitoring, and self-revision throughout the writing process. This research employed a qualitative descriptive-analytical approach through library research by analyzing various studies related to Arabic dictation, cognitive writing processes, the Flower and Hayes model, and AI-assisted language learning. The findings reveal that the stages of planning, translating, and reviewing in the Flower and Hayes model are closely related to orthographic activities in advanced Arabic dictation learning. The planning stage involves orthographic anticipation and linguistic retrieval before writing, the translating stage involves the transformation of mental representation into Arabic graphic forms, while the reviewing stage includes monitoring, evaluation, and orthographic revision. The study also indicates that AI can support these cognitive processes through predictive spelling, contextual prompting, real-time orthographic feedback, and automated revision assistance. Furthermore, AI contributes to the development of metacognitive awareness and self-monitoring during the writing process. This study concludes that integrating the Flower and Hayes cognitive writing model with AI-assisted learning may contribute to the development of more reflective, learner-centered, and cognitively oriented Arabic dictation instruction.
Enhancing Maharah Istima’ Through Roleplay and Focus Group Discussion: Implications for AI-Supported Language Learning
Arabic language learning in Indonesia continues to face challenges, particularly in developing students’ Maharah Istima’ (listening skills), which are often hindered by limited vocabulary, rapid speech input, and the dominance of passive instructional approaches. This study aims to examine the effectiveness of integrating Roleplay and Focus Group Discussion (FGD) in improving students’ listening ability within a classroom context. This research employed a quantitative approach using a quasi-experimental design with a non-equivalent control group. The participants consisted of 20 fifth-grade students at Al-Irhaam Global Islamic School Bandung, divided into an experimental group receiving Roleplay and FGD-based instruction and a control group receiving conventional audio-based instruction. Data were collected through pre-test and post-test and analyzed using Shapiro-Wilk normality test, homogeneity test, paired sample t-test, and N-Gain analysis. The findings indicate a statistically significant improvement in the experimental group (p < 0.05), with mean scores increasing from 72.0 to 88.0 and an N-Gain value of 0.63 (moderate category). Compared to the control group, the results suggest that interactive and collaborative learning activities contribute more effectively to the development of listening skills. These findings highlight the pedagogical potential of integrating Roleplay and FGD in promoting active engagement and deeper processing of language input. While this study is limited to a small-scale classroom context, it offers practical implications for designing interactive listening instruction and suggests opportunities for future integration with AI-supported language learning tools.
Arabic Scrabble as a Game-Based Learning Innovation for Vocabulary Mastery in AI-Oriented Arabic Education
This study investigates the implementation of Arabic Scrabble as a game-based learning innovation for enhancing Arabic vocabulary mastery among seventh-grade students at Darul Ihsan Modern Islamic Boarding School within the framework of AI-oriented Arabic education. The study responds to the continuing challenges of Arabic vocabulary learning, particularly students’ difficulties in memorizing and using vocabulary communicatively in classroom interaction. Using a qualitative phenomenological case study approach, data were collected through non-participant observation, semi-structured interviews, and documentation involving Arabic teacher and seventh-grade students. The findings reveal that the implementation of Arabic Scrabble created a more interactive and student-centered learning environment by increasing learner engagement, collaborative learning, vocabulary retention, and students’ confidence in using Arabic vocabulary. Through activities such as arranging Arabic letters into words, discussing meanings collaboratively, and constructing simple sentences, students became more active and motivated during the learning process. Furthermore, the study indicates that Arabic Scrabble reflects several characteristics associated with AI-oriented Arabic pedagogy, particularly interactive, adaptive, and engagement-based learning experiences. This study contributes to the discourse of game-based Arabic learning by positioning Arabic Scrabble not merely as an educational game, but as a pedagogical innovation that supports learner-centered Arabic education in modern Islamic boarding schools.
Fragmented Scaffolding in Arabic Textbooks: A Genre-Based Analysis of the Teaching Learning Cycle in Indonesian Madrasah Education
This study investigates the implementation of the Teaching Learning Cycle (TLC) in the Grade XI Arabic textbook published by KSKK Madrasah (2020). Using qualitative content analysis grounded in Systemic Functional Linguistics and Genre-Based Approach, the study analyzed six textbook chapters through the stages of BKOF, MOT, JCOT, and ICOT. AI-assisted comparative validation using ChatGPT-5 was additionally employed to support analytical consistency between manual and AI categorization results. The findings reveal that although all TLC stages formally appeared in the textbook, their pedagogical continuity was implemented inconsistently. BKOF and MOT generally provided adequate contextual and rhetorical support, while JCOT frequently lacked collaborative text construction and was often reduced to oral interaction. Consequently, several ICOT activities required students to produce independent texts without sufficient scaffolding. The study contributes to genre-based Arabic textbook evaluation within the context of Indonesian madrasah education.
Canva AI as a Cognitive Partner in Arabic Writing Instruction: Enhancing Linguistic Expression and Communicative Competence through Magic Write
The integration of Artificial Intelligence (AI) into language learning has introduced new opportunities for developing writing skills beyond conventional grammar-focused instruction. This study investigates the role of Canva AI, particularly its Magic Write feature, in enhancing Arabic writing (Maharah Kitabah) among university students. Using a qualitative descriptive approach, the study analyzes the transformation of students’ drafts into AI-assisted texts through documentation and content analysis. The primary data consist of Arabic writing products in the genres of condolence messages (ta'ziyah) and birthday greetings (yaumul milad), while relevant literature on AI-assisted writing and Arabic language learning serves as supporting data. The findings reveal that Canva AI contributes to Arabic writing development in two significant ways. First, it functions as a linguistic refinement tool by improving lexical choice, textual cohesion, pragmatic appropriateness, and stylistic quality. Second, it serves as an expressive enrichment tool by expanding semantic content, diversifying expressions, and enhancing the emotional and communicative dimensions of writing. The study further demonstrates that Canva AI operates not merely as a writing assistant but as a cognitive partner that supports students in producing more natural, contextually appropriate, and aesthetically appealing Arabic texts. These findings suggest that the integration of Generative AI into Arabic writing instruction can promote communicative competence, pragmatic awareness, and linguistic creativity while maintaining the learner’s central role in the writing process.
Arabic Orthography Instruction and Writing Accuracy among Madrasah Students: Insights for AI-Assisted Language Learning
The growing use of AI-assisted language learning highlights the importance of foundational writing competencies in Arabic language education. This study aims to investigate the implementation of Arabic writing instruction, identify students’ writing difficulties, and examine its relevance to AI-assisted language learning. Using a qualitative descriptive approach, the study was conducted at MTs Darul Irsyadiyah Labuhanbatu involving seventh-grade students and an Arabic language teacher. Data were collected through observations, interviews, and documentation and analyzed descriptively. The findings reveal that writing instruction was implemented systematically to improve students’ orthographic accuracy. However, students continued to experience difficulties in distinguishing visually similar Arabic letters, applying harakat accurately, and writing independently. Limited instructional time and insufficient writing practice were identified as major challenges. The study suggests that orthographic competence remains a fundamental prerequisite for effective Arabic writing, even in AI-assisted learning environments where automated feedback and writing support are increasingly available.
The Foundations of AI-Driven Arabic Learning Materials: Toward an Integrative Conceptual Framework
Artificial Intelligence (AI) has created new opportunities for developing Arabic learning materials that are more adaptive, personalized, and interactive. However, existing studies have primarily focused on technological applications, with limited attention to the theoretical foundations that should guide the development of AI-driven Arabic learning materials. This study aims to analyze the theoretical foundations underlying the development of Arabic learning materials in the era of artificial intelligence. Using a qualitative library research design, data were collected from scholarly books, journal articles, conference proceedings, and other relevant academic publications. The data were analyzed through content analysis, thematic analysis, and conceptual synthesis. The findings indicate that the development of AI-driven Arabic learning materials should be grounded in five interconnected foundations: philosophical, pedagogical, linguistic, psychological, and sociocultural. These foundations provide a comprehensive framework for integrating AI into Arabic language education while ensuring adaptability, personalization, interactivity, and cultural authenticity. This study contributes an integrative conceptual perspective that positions AI as an educational enabler rather than merely a technological tool.
Enhancing Arabic Speaking Skills Through AI-Assisted Adaptive Vocabulary Games: Evidence from a Classroom Intervention
The development of Arabic speaking skills remains a significant challenge for many secondary school learners, particularly due to limited vocabulary mastery and insufficient opportunities for meaningful oral communication. This study investigates the effectiveness of AI-assisted adaptive vocabulary games in enhancing Arabic speaking skills among secondary school students. Using a classroom intervention design conducted in two instructional cycles, the study involved 31 tenth-grade students at MA Muhammadiyah Kota Bima, Indonesia. Data were collected through speaking performance tests, classroom observations, and learning documentation. The findings revealed a consistent improvement in students’ speaking performance, as indicated by an increase in the mean score from 62 in the pre-intervention stage to 68 in Cycle I and 76.25 in Cycle II. Learning mastery also increased substantially from 16.7% to 80.64% by the end of the intervention. Furthermore, the N-Gain score of 0.375 indicated a moderate level of effectiveness. The findings suggest that vocabulary-centered interactive activities can facilitate oral language production by promoting contextual vocabulary use and repeated speaking practice. From an adaptive learning perspective, the study highlights the potential of integrating Generative Artificial Intelligence into vocabulary game design to support more personalized and communicative Arabic language learning environments. The study contributes to the growing discourse on AI-enhanced language learning by demonstrating the pedagogical value of adaptive vocabulary-based interventions for developing Arabic speaking skills.
Bridging Traditional and AI-Supported Arabic Language Learning: Evidence from Gallery Walk Activities among Young Learners
Foreign language anxiety is one of the affective factors that frequently hinders the success of Arabic language learning, particularly in speaking skills (maharah kalam). High levels of anxiety may reduce students’ participation in communicative activities and negatively affect their speaking performance. This study aimed to examine the effectiveness of the Gallery Walk method in reducing foreign language anxiety and improving the Arabic speaking skills of third-grade students at MI Miftahul Khoir 1 Karangrejo. The study employed a quantitative approach using a one-group pretest-posttest design. The participants consisted of 22 third-grade students selected through total sampling. Data were collected using a foreign language anxiety questionnaire adapted from the Foreign Language Classroom Anxiety Scale (FLCAS) and an Arabic speaking performance test. The data were analyzed using descriptive statistics, the Wilcoxon Signed Rank Test, and a paired sample t-test. The results revealed that the Gallery Walk method significantly reduced students’ foreign language anxiety (Z = -4.177; p < .001) and significantly improved their Arabic speaking skills (t = -17.648; p < .001). These findings indicate that Gallery Walk creates a more collaborative, interactive, and low-pressure learning environment that supports both affective development and speaking performance. Beyond its contribution to Arabic language instruction, the study highlights the potential of Gallery Walk as a pedagogical bridge between traditional Arabic language learning and emerging AI-supported language learning environments.
Integrating Artificial Intelligence into Arabic Textbook Evaluation: A Linguistic Analysis Based on Al-Fawzan's Framework
This study evaluates the linguistic dimension of Durus al-Lughah al-'Arabiyyah li Ghayr al-Natiqin Biha (Book I) based on Abdul Rahman Al-Fawzan’s framework and explores the role of artificial intelligence (AI) in supporting textbook evaluation. Using a qualitative content analysis approach, the study examined selected linguistic materials, including vocabulary, grammatical structures, dialogues, and reading texts. A human–AI collaborative procedure was employed, in which AI assisted in identifying linguistic patterns and organizing data, while pedagogical interpretation remained under the researcher’s responsibility. The findings indicate that the textbook strongly fulfills the criteria of using Standard Arabic without intermediary language and employing dialogue as the primary instructional entry point. However, the integration of language elements with language skills and the comprehensive treatment of language skills were only partially fulfilled. The study also demonstrates that AI can support textbook evaluation by facilitating the identification of recurring linguistic patterns and structural relationships. Nevertheless, pedagogical judgment and contextual interpretation still require human expertise. The findings suggest that AI is most effective as an analytical assistant rather than an autonomous evaluator in Arabic textbook evaluation.
Leveraging Artificial Intelligence-Supported Quizlet Flashcards to Enhance Arabic Vocabulary Acquisition among Madrasah Students
Arabic vocabulary acquisition remains a major challenge for many learners, particularly in contexts where instruction is dominated by memorization and teacher-centered approaches. This study aimed to investigate the implementation of Artificial Intelligence-Supported Quizlet Flashcards in enhancing Arabic vocabulary acquisition among eleventh-grade students at MA Muhammadiyah Kota Bima. The study employed a Classroom Action Research (CAR) design based on the Kemmis and McTaggart model, consisting of planning, action, observation, and reflection stages conducted across two cycles. The participants were 12 students. Artificial Intelligence was utilized as a supporting tool for developing vocabulary materials, contextual example sentences, and learning exercises that were subsequently integrated into Quizlet flashcards. Data were collected through observation, tests, and documentation and analyzed using descriptive quantitative techniques. The findings revealed a consistent improvement in students’ vocabulary acquisition throughout the research cycles. The mean score increased from 67 in the pre-cycle stage to 75.17 in Cycle I and 80.67 in Cycle II. Similarly, the learning mastery rate improved from 16.7% in the pre-cycle stage to 50% in Cycle I and reached 83.33% in Cycle II. These findings indicate that AI-Supported Quizlet Flashcards can provide a more interactive, flexible, and engaging learning experience, thereby supporting students’ Arabic vocabulary acquisition. The study highlights the potential of integrating AI-assisted learning-material development with digital flashcard platforms to enhance Arabic language learning in madrasah settings.
AI-Driven Transformation of Arabic Language Curriculum: Challenges and Opportunities in Digital Learning
Artificial Intelligence (AI) has transformed educational practices and created new challenges and opportunities for curriculum development, including in Arabic language education. This study aims to analyze the challenges of digital learning in Arabic language education, examine the need for AI-driven curriculum transformation, and explore opportunities for integrating AI into curriculum development. Using a qualitative descriptive approach with a library research method, this study analyzed relevant literature on AI, digital learning, curriculum transformation, and Arabic language education. The findings reveal that digital learning challenges include curriculum rigidity, changing learner characteristics, limited digital competencies among educators, and technological infrastructure issues. The study also shows that AI has become a catalyst for curriculum transformation by influencing learning objectives, competencies, instructional strategies, and assessment systems. Furthermore, AI offers opportunities to enhance Arabic language learning through adaptive learning, intelligent tutoring systems, automated feedback, and technology-based assessment. The study concludes that future Arabic language curricula should integrate linguistic competence with digital literacy, AI literacy, critical thinking, and ethical technology use. This study contributes to the discourse on AI in education by positioning AI as a driving force for curriculum transformation in Arabic language education.
Artificial Intelligence and Local Wisdom Integration in Maharah Qira'ah Learning: A Systematic Literature Review
This study aims to synthesize the literature on the integration of Artificial Intelligence (AI), culture-based learning, and Maharah Qira’ah. Using a Systematic Literature Review (SLR) approach, 30 articles published between 2019 and 2026 were selected through predefined inclusion and exclusion criteria and analyzed using thematic synthesis. The findings indicate that AI supports personalized learning through adaptive instruction, automated feedback, and learning analytics. Culture-based learning enhances contextual relevance, learner engagement, and cultural identity, while digital innovations contribute to the development of reading literacy, reading comprehension, and critical reading skills. The review further reveals a strong conceptual relationship among these domains and proposes the concept of AI-Assisted Culture-Based Maharah Qira’ah Learning as a framework for adaptive, contextual, and culturally responsive Arabic reading instruction. This study contributes by bridging three research domains that have largely been examined separately and provides directions for future innovation in Arabic language education.
Metacognitive Strategies and Arabic Vocabulary Acquisition Through Listening Skills: Insights for AI-Assisted Language Learning
Vocabulary acquisition is a fundamental aspect of Arabic language learning, particularly in listening activities (maharah al-istimÄ’). This study explores the metacognitive strategies employed by students in acquiring Arabic vocabulary through listening skills and identifies the most dominant strategy used during the process. Using a descriptive qualitative approach, data were collected from students of the Arabic Language Education Program at the State Islamic University of Palangka Raya through observation, in-depth interviews, and documentation. The data were analyzed using the interactive model of Miles, Huberman, and Saldaña. The findings revealed that students employed three metacognitive strategies: planning, monitoring, and evaluating. During planning, students prepared their focus and learning resources. During monitoring, they took notes on unfamiliar vocabulary, replayed audio, inferred meanings from context, and sought assistance when needed. During evaluating, they reviewed notes, consulted dictionaries, discussed with peers, and replayed audio to confirm understanding. Monitoring emerged as the most dominant strategy. The findings indicate that metacognitive strategies support more systematic and autonomous vocabulary acquisition and provide implications for the development of AI-assisted Arabic language learning environments.
Integrating Active Learning within an AI-Supported Gamified Environment through Zep Quiz to Improve Arabic Language Achievement
This study investigates the integration of Active Learning into an AI-supported gamified environment through Zep Quiz to improve Arabic language achievement among seventh-grade students at MTs Negeri 1 Bandar Lampung. The study employed Classroom Action Research (CAR) based on the Kemmis and McTaggart model, consisting of two cycles involving 32 students. Data were collected through classroom observations, learning achievement tests, and documentation. Quantitative data were analyzed using mean scores, classical mastery percentages, and N-Gain, while qualitative data were analyzed through observations of student engagement during the learning process. The findings reveal that the integration of Active Learning and Zep Quiz successfully enhanced both student engagement and Arabic language achievement. The average score increased from 52.38 in the pre-cycle stage to 78.13 in Cycle I and 87.50 in Cycle II. Similarly, classical mastery improved from 28.13% to 65.63% and eventually reached 90.63%, exceeding the predetermined success criterion of 85%. The N-Gain score of 0.74 indicates a high level of improvement. These findings suggest that AI-supported gamified learning environments can effectively support Active Learning and foster meaningful improvements in Arabic language learning outcomes.
Revisiting Ahmad Amin's Literary Criticism in the Age of Artificial Intelligence: Bridging Evaluative Tradition and Interpretive Approaches
This study revisits Ahmad Amin’s concept of literary criticism through his seminal work Al-Naqd al-Adabī and examines its relevance in the age of Artificial Intelligence. Employing library research with an intellectual history approach and qualitative content analysis, the study reconstructs Ahmad Amin’s critical framework and situates it within the development of modern Arabic literary criticism. The findings reveal that Ahmad Amin conceptualizes literary criticism as an intellectual activity that integrates interpretation and evaluation rather than privileging one over the other. He argues that critical judgment should emerge from a thorough understanding of literary texts and be guided by objectivity, aesthetic sensitivity (dhawq), broad knowledge, and intellectual integrity. The study further demonstrates that Ahmad Amin occupies a transitional position between the evaluative tradition of classical Arabic criticism and the interpretive approaches of modern literary criticism. In contemporary contexts, his ideas offer a valuable framework for understanding the role of human judgment in AI-assisted literary criticism. While artificial intelligence can support textual interpretation and analysis, aesthetic evaluation and intellectual responsibility remain fundamentally human domains. These findings highlight the continuing relevance of Ahmad Amin’s literary-critical thought for contemporary literary studies.
Bridging Traditional Arabic Language Curriculum and Artificial Intelligence-Based Learning: An Analysis of Dar Al-Yusr Islamic Boarding School
The rapid advancement of Artificial Intelligence (AI) has transformed language education, creating opportunities to integrate technology with established curriculum models. This study analyzes the Arabic language curriculum implemented at Dar Al-Yusr Islamic Boarding School and examines its relevance to AI-based language learning. Employing a qualitative case study design, data were collected through curriculum document analysis, classroom observations, and semi-structured interviews with school leaders and Arabic language teachers. The data were analyzed using the interactive model of Miles, Huberman, and Saldaña and interpreted through the perspective of AI-supported language learning. The findings indicate that the curriculum is systematically organized around four interconnected components: learning objectives, curriculum content, instructional strategies, and assessment practices. The curriculum emphasizes linguistic competence as the foundation for understanding Islamic classical texts while integrating communicative skills through progressive learning stages. Furthermore, its structured competency progression, coherent curriculum architecture, and continuous assessment demonstrate strong pedagogical characteristics that align with adaptive and personalized AI-supported learning. The study concludes that traditional Arabic curricula can serve as a robust pedagogical foundation for AI integration, provided that digital literacy and AI-oriented instructional innovations are incorporated without compromising the educational values of Islamic boarding schools.
Artificial Intelligence as Linguistic Scaffolding in MahÄrah KalÄm Learning: Addressing Lexical and Morphosyntactic Challenges
The rapid development of generative artificial intelligence (AI) has created new opportunities for supporting foreign language learning, including Arabic speaking (MahÄrah KalÄm). However, empirical evidence explaining how students utilize AI to address linguistic challenges during oral language production remains limited. This study explores the use of AI in supporting university students' Arabic speaking learning, focusing on linguistic challenges, AI-assisted learning practices, and perceived changes in learning experiences. A qualitative case study was conducted with ten undergraduate students enrolled in the Arabic Language Education Program at Universitas Islam Negeri Salatiga, Indonesia. Data were collected through semi-structured interviews and participant observation and analyzed using thematic analysis. The findings reveal three dominant forms of AI utilization: supporting vocabulary retrieval, assisting sentence construction based on nahwu and ṣarf, and facilitating students' preparation before speaking activities. The study also indicates that AI helps students access linguistic support more efficiently and enhances their readiness for classroom speaking practices. Nevertheless, lecturer mediation remains essential to validate AI-generated outputs and prevent excessive reliance on technology. These findings suggest that AI should be integrated as a pedagogical support tool rather than a substitute for language instruction in Arabic speaking classrooms.
Master's Students' Perceptions of Instagram for Self-Directed Arabic Learning in the Age of Artificial Intelligence
The increasing use of social media has created new opportunities for self-directed language learning beyond formal classrooms. However, little is known about how postgraduate students perceive Instagram as a medium for self-directed Arabic language learning. This study explores master's students' perceptions of Instagram and identifies its perceived benefits and challenges. A qualitative descriptive approach was employed involving master's students in the Teaching Arabic to Non-Native Speakers program at the International Islamic University Malaysia. Data were collected through semi-structured interviews and analyzed using thematic analysis. The findings indicate that students generally perceive Instagram positively because it provides flexible access to authentic learning resources, promotes continuous language exposure, and supports learner autonomy. The platform also enhances vocabulary development, listening skills, motivation, and learning flexibility. Nevertheless, digital distractions, fragmented learning content, and varying content quality remain significant challenges, highlighting the importance of self-regulation and digital literacy. This study demonstrates that Instagram's educational value lies in fostering learner autonomy and provides practical insights for integrating social media into Arabic language learning in the AI era.
Reassessing Uslūb IstifhÄm in MukhtÄr al-Aá¸¥ÄdÄ«á¹¡ an-Nabawiyyah: Implications for Artificial Intelligence Interpretation
This study examines the relationship between syntactic structure and rhetorical meaning in uslūb istifhÄm within MukhtÄr al-Aá¸¥ÄdÄ«á¹¡ an-Nabawiyyah by integrating naḥw and 'ilm al-ma'ÄnÄ«. Previous studies have primarily focused on identifying interrogative particles or classifying rhetorical meanings, while the interaction between syntactic structure and contextual meaning remains underexplored. Employing a qualitative library research design, this study analyzes 45 interrogative expressions identified from 183 hadiths in KhÄtimÄtun fī Taá¸hÄ«b an-Nufūs. Data were collected through documentation and analyzed using syntactic and rhetorical approaches. The findings reveal that interrogative constructions function predominantly as istifhÄm ghair ḥaqīqī, expressing rhetorical meanings such as at-tashwīq, at-taqrīr, al-amr, al-inkÄr, at-ta'ajjub, at-ta'ẓīm, and at-tanbīh 'alÄ á¸alÄl. These meanings are determined not solely by grammatical structure or interrogative particles but through the interaction between syntax, siyÄq al-kalÄm, qarīnah, and communicative purpose. This study proposes an integrated analytical framework that enriches Arabic linguistic studies and provides a conceptual foundation for context-sensitive AI-assisted hadith interpretation.
Applying Gérard Genette's Focalization Theory to Jinn: A Study of Fear Construction with Implications for AI-Based Narrative Interpretation
This study examines the construction of fear in the Arabic television series Jinn through Gérard Genette's theory of focalization. Employing a qualitative descriptive approach, the research analyzes four selected scenes using purposive sampling. Data were collected through observation and documentation, focusing on dialogue, visual narration, camera perspective, and character expressions. The analysis applies Genette's three focalization categories, zero, internal, and external focalization, to identify how narrative information is distributed and how fear is constructed through perspective. The findings reveal that zero focalization establishes anticipatory fear by providing the audience with information unavailable to the characters, internal focalization intensifies psychological fear through restricted perception, and external focalization generates suspense by limiting access to characters' inner states. These findings demonstrate that fear in Jinn is primarily produced through narrative information management rather than the mere presence of supernatural elements. Beyond contributing to Arabic narratology, this study offers conceptual insights for AI-based narrative interpretation by highlighting focalization as a systematic framework for detecting narrative perspective, information distribution, and emotional construction in audiovisual narratives.