AI in Arab Universities: Challenges and Opportunities for English Majors

The Digital Polyglot: A Comparative-Argumentative Analysis of Artificial Intelligence Integration in Undergraduate English Majors Across Arabic Universities 

Abstract 

The rapid proliferation of Artificial Intelligence (AI) has precipitated a paradigm shift in higher education globally, with profound and specific implications for English as a Foreign Language (EFL) and English Literature departments in the Arab world. This article provides a comprehensive comparative-argumentative analysis of AI utilization—specifically Generative AI (GenAI) tools like ChatGPT, neural machine translation engines, and automated feedback systems—among undergraduate English majors in Arabic universities. Drawing on recent empirical data from Saudi Arabia, Libya, Jordan, and the broader Middle East and North Africa (MENA) region, the study juxtaposes the pedagogical benefits of personalized scaffolding and anxiety reduction against the critical challenges of academic integrity, over-reliance, and infrastructural inequity. The article argues that while AI threatens traditional assessment models, its integration is not only inevitable but essential for transitioning Arab EFL curricula from rote memorization to critical digital literacy. A balanced pedagogical framework, termed “Critical AI-Literacy Integration,” is proposed to harness these tools effectively while preserving human cognitive agency and cultural identity. 

Introduction 

The landscape of English Language Teaching (ELT) in the Arab world is currently undergoing a seismic transformation driven by the Fourth Industrial Revolution. For decades, undergraduate English programs in many Arabic universities have grappled with systemic challenges such as large class sizes, varying student proficiency levels, and a historical reliance on teacher-centered, grammar-translation methodologies. However, the advent of Generative AI (GenAI) and Large Language Models (LLMs) has introduced a disruptive variable that promises to democratize access to high-quality language input while simultaneously threatening the integrity of traditional academic outputs. The integration of these technologies is not merely a technical upgrade but a complex socio-educational negotiation that challenges the very definition of authorship and language mastery in the twenty-first century. 

The context of the Arab world is unique due to the phenomenon of diglossia and the specific cultural prestige attached to English proficiency as a gateway to the global economy. As noted by recent scholarship, the integration of AI in this region is laden with specific socio-cultural implications. In the Gulf Cooperation Council (GCC) countries, such as Saudi Arabia and the United Arab Emirates, national strategic frameworks like “Vision 2030” have accelerated digital adoption, creating a fertile ground for AI integration (Al-Jarf, 2024). Conversely, in contexts like Libya, Sudan, and Yemen, infrastructural limitations and political instability pose significant barriers to equitable AI access, creating a stark digital divide within the region itself (Hadaga & Elfalfal, 2025). 

This article posits that the wholesale banning of AI in Arabic English departments is both futile and pedagogically detrimental. Instead, a comparative analysis of current implementations reveals that AI serves as a potent “scaffold” for lower-proficiency learners, reducing affective filters and enhancing writing coherence. However, without rigorous pedagogical oversight, it risks fostering a culture of “surface learning” where the process of language acquisition is bypassed in favor of instant product generation. By examining the utilization of AI across the key domains of academic writing, translation, and oral proficiency, this paper argues for a shift towards “Critical AI-Literacy,” where students are taught to collaborate with AI rather than capitulate to it. 

Theoretical Framework: Technology Acceptance in the Arab Context 

To understand the adoption of AI among Arab undergraduates, it is essential to employ the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework. Recent studies in Saudi Arabia indicate that “Perceived Usefulness” (PU) and “Perceived Ease of Use” (PEOU) are the primary drivers of AI adoption among EFL students (Fayed & Al-Ghamdi, 2025). Arab students, often digital natives, perceive tools like ChatGPT not primarily as cheating mechanisms, but as necessary aids for overcoming the significant linguistic gap between their native Arabic and academic English. The perceived usefulness is amplified in the Arab context by the distinct rhetorical differences between Arabic and English; students often struggle to map the circular, ornate rhetorical patterns of Arabic onto the linear, direct argumentation required in English academic writing. AI tools, which naturally default to standard English rhetorical structures, offer an immediate solution to this cognitive dissonance (Alqaed, 2024). 

However, the TPACK framework highlights a critical gap in faculty readiness. While students are rapidly adopting these tools, faculty members in regions like Libya and Jordan often rely on self-study rather than formal institutional training to understand AI (Hadaga & Elfalfal, 2025). This leads to a pedagogical dissonance where students use AI to facilitate learning, while instructors view such use solely through the lens of academic dishonesty. The disparity in technological pedagogical knowledge between students and faculty threatens to undermine the trust necessary for effective education. Therefore, any discussion of AI in Arab universities must account for this generational and technical gap, recognizing that the resistance to AI is often rooted in a lack of professional development rather than inherent pedagogical flaws in the technology itself (Radwan, 2025). 

Comparative Analysis of AI Utility in EFL Skills 

Academic Writing: Scaffolding versus Ghostwriting 

The most contentious arena for AI in English majors is academic writing. Traditionally, writing instruction in Arab universities has focused heavily on grammatical accuracy, often penalizing errors so severely that students become risk-averse. The introduction of AI tools has fundamentally altered this dynamic. 

On one hand, the argument for scaffolding suggests that AI tools act as “always-on” tutors that can guide students through the writing process. Research involving Saudi ESL students has demonstrated that ChatGPT provides personalized feedback that significantly improves writing proficiency, vocabulary expansion, and learner autonomy (Abduljawad, 2025). For the Arab learner, who may lack exposure to authentic English environments outside the classroom, AI provides a continuous stream of high-quality linguistic input. The tool allows students to bypass the “blank page syndrome,” offering outlines and structural suggestions that are particularly helpful for those struggling with the structural requirements of the English essay. Furthermore, AI can serve as a corrective mechanism for “Arablish”—the interlanguage errors caused by literal translation from Arabic—by explaining why a certain phrasing is incorrect, rather than simply marking it wrong as a human grader might due to time constraints (Alhamam, 2025). 

On the other hand, the counter-argument centers on dependency and cognitive offloading. Critics argue that the ease of AI generation leads to a bypass of the “productive struggle” necessary for language acquisition. While AI feedback is effective, there is a significant risk of students accepting AI suggestions uncritically, leading to a homogenization of student voice. In Jordan and Algeria, studies comparing AI feedback to teacher feedback found that while AI is a scalable solution for large classes, it lacks the nuance of human mentorship and often fails to address deep-level content issues, focusing instead on surface-level coherence (Benali & Ziani, 2025). The danger is that students will produce essays that are grammatically perfect but intellectually hollow, masking their true proficiency levels and preventing educators from identifying and addressing genuine learning gaps (Hassan, 2025). 

The synthesis of these positions suggests that AI is most effective when used iteratively. Students who use AI to revise rather than generate text show marked improvement in coherence and cohesion. The pedagogical goal, therefore, must be to design assignments that require evidence of the writing process—such as prompt engineering logs and draft comparisons—rather than evaluating the final product alone. 

The Translation Transformation: From Decoding to Post-Editing 

Translation courses are staples of English majors in the Arab world, given the geopolitical importance of the region. The integration of AI has necessitated a shift from traditional “translation” to “post-editing.” 

Traditionally, translation pedagogy in Arab universities emphasized manual dictionary use and rote memorization of equivalent terms. However, students are increasingly using Neural Machine Translation (NMT) tools like DeepL and ChatGPT. A study on translation students found that a vast majority utilize AI tools to ensure consistency and efficiency (Yousef, 2025). The argument here is that AI handles the tedious aspects of translation—morphological agreement and syntactic ordering—freeing the student to focus on higher-order semantic transfer (Bowker, 2020). 

However, the limitations of AI in the Arab context are profound. Arabic is a high-context language with a rich system of derivation and polysemy. AI tools frequently misinterpret cultural idioms, religious subtexts, and dialectal variations, producing translations that are technically correct but pragmatically disastrous. For example, AI may fail to capture the appropriate register when translating formal Islamic texts or contemporary Arabic literature (Karim & Hassan, 2025). Consequently, the value of the human translator in the Arab context is shifting toward cultural localization. The curriculum must therefore evolve to teach students how to audit AI output for cultural bias and accuracy, transforming them from mere translators into sophisticated editors who bridge the gap between AI capability and human nuance (Karim & Hassan, 2025). 

Oral Proficiency and Anxiety Reduction 

Speaking is often cited as the most anxiety-inducing skill for Arab EFL learners due to the cultural emphasis on “face-saving” and the fear of public embarrassment. In traditional classrooms, large student numbers often mean that individual speaking time is negligible, and the fear of making errors in front of peers inhibits participation. 

AI chatbots and voice-interactive systems provide a judgment-free zone that directly addresses this affective barrier. Research from Libya indicates that teachers observed reduced anxiety among students who practiced with AI interlocutors (Zaid, 2025). These tools allow for infinite repetition and immediate pronunciation correction without the social pressure of the classroom. For a student in a remote area of Sudan or Egypt, who may never encounter a native English speaker, an AI voice assistant serves as a viable surrogate for conversational practice, offering exposure to various accents and speeds of delivery (Mahmoud, 2025). 

However, the limitation of this approach is the lack of authentic socio-pragmatic complexity. Faculty members have expressed concerns that while AI promotes fluency and confidence, it cannot fully replicate the chaotic, turn-taking dynamics of real human conversation. AI interactions are often transactional and linear, lacking the interruptions, non-verbal cues, and emotional intelligence of human dialogue (Mahmoud, 2025). Therefore, while AI is an excellent tool for drilling and confidence building, it cannot replace the interactive classroom tasks that teach students how to negotiate meaning in real-time. 

Regional Disparities and Infrastructural Challenges 

A comparative view of the Arab world reveals a stark “digital divide” that dictates the feasibility of AI integration. The experience of an English major in Riyadh is vastly different from one in Tripoli or Khartoum. 

In the Gulf context, universities are increasingly integrating AI into their Learning Management Systems (LMS). With robust internet infrastructure and high student ownership of personal devices, the challenges in Saudi Arabia and the UAE are primarily pedagogical and ethical—how to regulate abundance and focus attention (Dahmash, 2024). Universities here are in a position to purchase institutional licenses for advanced AI tools and provide comprehensive faculty training. 

In contrast, the North African context faces foundational challenges. In Libya and Sudan, while student attitudes toward AI are positive, the lack of consistent electricity, robust internet connectivity, and access to paid AI versions creates a bottleneck (Khalil, 2025). Students often have the will but lack the institutional support to use these tools effectively. Furthermore, the “training gap” is more pronounced in these regions, where faculty may view AI as an imposition from the West that exacerbates existing inequalities (Hadaga & Elfalfal, 2025). For these universities, the priority is not advanced AI integration but basic digital literacy and access. The disparity suggests that a “one-size-fits-all” approach to AI in the Arab world is impossible; strategies must be localized to the infrastructural realities of each institution. 

The Ethical Dimension: Academic Integrity and Cultural Bias 

The most significant argument against AI in Arabic universities revolves around academic integrity. There is a pervasive fear among faculty that AI facilitates sophisticated cheating, termed “Plagiarism 2.0.” A systematic review of GenAI in higher education highlights that while AI enhances engagement, it poses significant risks to the fundamental tenets of academic honesty (El-Sawy, 2025). In the Arab educational culture, which has historically valued the reproduction of knowledge and respect for authority, the line between “using a reference” and “AI generation” is blurring. Without clear guidelines, students may inadvertently commit plagiarism believing they are simply utilizing a modern study aid (Ahmad & Al-Khanjari, 2024). 

Furthermore, there is an ethical dimension related to cultural bias. Most Large Language Models are trained primarily on Western datasets, dominated by English and Western cultural norms. Arab students relying heavily on these tools may inadvertently adopt Western-centric perspectives, idioms, and cultural values, potentially eroding their own cultural voice in English writing (Jaffar & Nazrol, 2025). This “epistemic colonization” is a subtle but dangerous consequence of uncritical AI adoption. For instance, an AI might suggest removing a culturally specific metaphor in a creative writing assignment because it does not align with standard American English usage. It is imperative, therefore, that educators teach students to recognize these biases and to use AI to enhance their expression without erasing their cultural identity (Al-Shamrani, 2024). 

Pedagogical Recommendations: Towards Critical AI-Literacy 

Based on this comparative analysis, English departments in Arabic universities must move beyond binary debates of prohibition versus adoption. The following pedagogical framework is proposed to facilitate “Critical AI-Literacy.” 

First, institutions must redefine learning outcomes. The objective of an English degree can no longer be merely the production of grammatically correct text, as machines can now achieve this instantly. Instead, the focus must shift to critical evaluation, editing, and prompt engineering. Assessments should be designed to evaluate the process of writing and translation. For example, students could be required to submit the initial AI output, their critique of its flaws, and their final polished version, with grades awarded for the quality of the critique and the improvement made (Qadhi & Al-Qurashi, 2025). 

Second, hybrid assessment models are essential to verify authentic competency. While AI can be used for formative assessments such as drafting and brainstorming, summative assessments should reintroduce oral exams and in-class writing where AI aids are restricted. This ensures that students retain the fundamental cognitive skills of language production (Dahmash, 2024). 

Third, faculty development must be prioritized. Universities must invest in training that moves beyond “how to detect AI” to “how to teach with AI.” Faculty need to understand the capabilities and “hallucinations” of these models to guide students effectively. This includes training on the ethical implications of AI and strategies for integrating it into lesson plans without compromising academic rigor (Radwan, 2025). 

Finally, localized AI ethics policies are crucial. Universities must develop clear, institution-specific policies that define the difference between “collaboration” with AI and “plagiarism.” These policies should be co-created with students to ensure buy-in and understanding (Saleh, 2025). 

Conclusion 

The integration of AI in undergraduate English majors across Arabic universities is not a future possibility but a present reality. The evidence from across the region suggests that students are already leveraging these tools to bypass traditional linguistic barriers and navigate the demands of their curricula. The academic argument, therefore, must shift from resistance to resilience. AI offers unprecedented opportunities to scaffold learning, reduce anxiety, and democratize access to English proficiency. However, these benefits are contingent upon a pedagogical approach that foregrounds human agency and critical thinking. By embracing a “Critical AI-Literacy” approach, Arabic universities can empower students to become not just consumers of English, but sophisticated, tech-savvy communicators capable of navigating the global digital landscape while retaining their unique cultural voice. The future of ELT in the Arab world lies not in competing with machines, but in mastering the art of collaborating with them. 

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