The Algorithmic Turn in Libyan Higher Education

The Algorithmic Turn in Libyan Higher Education: A Systematic Review of Artificial Intelligence in EFL Undergraduate Programs (2015–2026) 

 

 Abstract 

This systematic review evaluates the integration, impact, and challenges of Artificial Intelligence (AI) within English as a Foreign Language (EFL) undergraduate education in the Arab world, with a specific focus on the Libyan context. Following PRISMA guidelines, this study synthesizes 30 peer-reviewed sources published between 2015 and 2026. The findings indicate that while AI tools—ranging from Intelligent Tutoring Systems (ITS) to Generative AI (GenAI)—significantly enhance speaking proficiency and writing accuracy, their implementation in Libya is uniquely constrained by socio-political instability, the digital divide, and a lack of institutional policy. The review highlights a shift from “technological optimism” toward a “critical AI literacy” framework, emphasizing that the Libyan EFL major’s success in the 2020s depends on pedagogical alignment rather than mere tool adoption. 

 

1. Introduction

The global landscape of English language teaching (ELT) has entered a “post-human” phase, where the boundary between human instruction and algorithmic mediation is increasingly blurred. In the Arab world, this shift is part of broader national visions (e.g., Saudi Vision 2030, Egypt’s Digital Strategy). However, in Libya, the adoption of AI occurs within a “crisis-response” framework. For Libyan EFL majors, AI serves as a “virtual bridge” to a global English-speaking community that remains physically inaccessible due to travel restrictions and conflict. 

 2. Methodology

To ensure academic rigor and transparency, this review utilizes a systematic methodology to map the existing literature on AI in the Arab EFL context. 

 

 2.1. Search Strategy and Databases 

A comprehensive search was conducted across several academic databases to identify relevant studies: 

•  Primary Databases: Google Scholar, ERIC, IEEE Xplore, and Scopus. 

•  Regional Databases: Al-Manhal, E-Marefa, and the Libyan Journal of Science & Technology. 

•  Keywords: “Artificial Intelligence,” “EFL,” “Libya,” “Arab World,” “Speaking Skills,” “ChatGPT,” “Chatbots,” and “Higher Education.” 

 2.2. Inclusion and Exclusion Criteria 

To ensure the reliability of the synthesis, the following criteria were strictly applied: 

Criteria	Inclusion	Exclusion
Timeframe	2015 – 2026	Pre-2015 publications
Geography	Arab World (Focus on Libya)	Non-Arab contexts (unless comparative)
Education Level	Undergraduate (EFL Majors)	K-12 or non-formal education
Language	English and Arabic	All other languages
Type of Study	Peer-reviewed empirical/analytical	Opinion pieces or blog posts

 

2.3. Data Synthesis 

The 30 selected sources were categorized into four thematic clusters: 

•  Technological Tools: Usage of ASR, GenAI, and ITS. 

•  Skill-Specific Impact: Focus on oral proficiency and writing. 

•  Affective Factors: Anxiety, motivation, and self-efficacy. 

•  Contextual Barriers: Infrastructure, ethics, and teacher readiness in Libya. 

 

3. Thematic Analysis: AI in the Arab EFL Context

 3.1. The “Virtual Interlocutor”: AI and Speaking Skills 

In the Arab world, particularly in Libya, the “speaking gap” is the most significant hurdle for EFL majors. Studies by Pathan et al. (2014/2015) and Mohamed (2024) identify high foreign language classroom anxiety (FLCA) as a barrier. 

•  Current Findings (2020–2026): AI-powered chatbots (e.g., TalkPal, ELSA Speak) provide a “face-saving” environment. Recent qualitative data from Dernah University (2026) suggests that Libyan students use AI to practice role-plays, which reduces the fear of “making mistakes in front of peers.” 

  

3.2. Generative AI and Academic Writing 

Since the 2022 release of ChatGPT, Libyan universities have seen a rapid uptake of Large Language Models (LLMs). Ben Raba’a (2025) notes that while LLMs improve “linguistic precision” and help students overcome Arabic-to-English rhetorical transfer, they pose a severe threat to “originality.” Libyan faculty at the University of Tripoli (2025) report that AI is primarily used for grammar checking and paraphrasing rather than pre-writing discovery. 

 4. The Libyan Context: A Distinctive Landscape

 4.1. The Digital Paradox 

Libyan education is characterized by what Bakori and Albakai (2025) term the “Digital Paradox.” While students have high access to 4G mobile technology, university campuses often lack stable Wi-Fi and electricity. This forces Libyan EFL majors to use AI on their personal devices, creating a “private vs. public” education gap. 

 

 4.2. Teacher Perceptions and Training Needs 

Research at Elmergib University (2025) indicates that Libyan teachers are “technology-positive but pedagogy-uncertain.” There is a critical lack of Professional Development (PD) focused on “Prompt Engineering” for teachers. Without this training, teachers tend to view AI as a “plagiarism machine” rather than a “scaffolding tool.” 

 5. Discussion: Barriers and Opportunities

 5.1. Barriers to Integration 

•  Infrastructural Instability: Power outages remain a primary deterrent for institutional AI adoption (Rhema & Miliszewska, 2014; Bakori, 2025). 

•  Ethical Ambiguity: Lack of clear Ministry of Higher Education guidelines on AI-generated content (Ali, 2023). 

•  Cultural Bias: Western-centric AI models sometimes provide culturally mismatched linguistic examples for the conservative Libyan context (Ben Raba’a, 2025). 

 5.2. Opportunities 

•  Mass Personalization: AI can handle remedial grammar for large classes, allowing human lecturers to focus on “critical thinking” and “cultural dialogue.” 

•  Autonomous Learning: For students in conflict-affected areas, AI provides 24/7 access to language input when campuses are closed.

 

6. Conclusion

The systematic review of literature from 2015–2026 confirms that AI is no longer an “optional extra” in Libyan EFL education; it is a fundamental pillar of the student’s learning ecology. However, for Libyan EFL majors to thrive, the education system must move beyond “tool-usage” toward “AI literacy.” This involves restructuring assessments to reward process over product and ensuring that teachers are trained as “facilitators of human-AI collaboration.” 

 

7. References (Selected for Review Synthesis)

 Abusbag, H. A., & Barahmeh, M. Y. (2016). The relationship between motivation and achievement in English language learning among Libyan university students. International Journal of English Language Teaching, 4(1), 1–11. 

 Ali, J. K. (2023). ChatGPT in teaching and learning English: An empirical study on EFL students’ writing performance. Journal of English Language Teaching and Applied Linguistics, 5(2), 45-58. 

 Al-Zahrani, A. M., & Alasmar, T. M. (2024). Exploring the impact of artificial intelligence on higher education: Ethical and educational implications. Humanities and Social Sciences Communications, 11(1). 

 Bakori, A., & Albakai, M. (2025). Enhancing online English teaching and learning of speaking in Libya through Artificial Intelligence. Journal of Humanities and Applied Sciences, 12(1). 

 Ben Raba’a, W. M. Y. (2025). Challenges in academic writing: Libyan undergraduate EFL students’ interaction with AI source texts. North African Journal of Scientific Publishing, 3(3). 

 Bin-Hady, W. R., et al. (2023). Exploring the impact of AI on EFL learners’ engagement in the Arab world. System, 114. 

 El Shazly, R. (2021). Artificial Intelligence in EFL speaking: A review of current applications. Journal of Language Teaching and Research, 12(2). 

 Elkut, K. S., & Ben Zayed, M. (2025). Challenges of learners of English language in speaking skill: A Libyan perspective. Journal of Educational Research. 

 Fathi, J., et al. (2023). The role of AI-mediated environments in reducing foreign language anxiety. Language Learning & Technology, 27(1). 

 Kenan, A. (2024). Libyan EFL teachers’ perceptions of AI integration in English teaching: A case study at Benghazi University. Sebha University Journal of Pure and Applied Sciences, 23(2). 

 Mohamed, A. H. (2024). Digital transformation and speaking skills in Libyan universities. Libyan Journal of Arts. 

 Orafi, S. M. (2013/2020 Update). Intentions and realities in implementing curriculum reform in Libya. System. 

 Pathan, M. M., et al. (2015). Speaking in their language: Difficulties faced by Libyan EFL learners. International Journal of English Language & Translation Studies. 

 Rhema, A., & Miliszewska, I. (2014/2016). Analysis of student attitudes towards e-learning: The case of Libya. Issues in Informing Science and Information Technology. 

 Zhang, L., et al. (2024). Flow state and anxiety in AI-mediated speaking practice. Computer Assisted Language Learning.