Abstract

This paper attempted to conduct a critical analysis of Donald Trump’s inauguration speech in 2025. The speech was analysed through Van Dijk’s socio-cognitive approach. Three levels of analysis were employed: textual, cognitive, and social. The findings revealed that Trump employed several rhetorical appeals, including logos and pathos. Additionally, various rhetorical devices such as hyperbole, metaphors, and parallel structures were utilised to reinforce his ideas. The analysis also revealed underlying ideologies within the speech, including nationalism, inequality, and protectionism. Trump depicted Americans as law-abiding citizens, while he portrayed immigrants as criminals posing a serious threat to America. Furthermore, Trump effectively employed linguistic tools and rhetorical devices to characterise Biden’s administration as weak and incompetent in managing social and economic issues, while presenting himself as a redeemer of America.

This study presents a quantitative descriptive analysis comparing the performance of human simultaneous interpreters (H-SI) and AI-driven interpreting systems (AI-SI) using deep learning architectures specifically Transformer-based models (e.g., mBART, Wav2Vec 2.0 + NMT pipelines) in real-time multilingual contexts. Drawing on a corpus of 1,248 interpreted segments extracted from TED Talks (English–Spanish, English–Mandarin), we evaluate output quality across six empirically derived metrics: lexical accuracy, syntactic fluency, temporal alignment, semantic fidelity, discourse cohesion, and error density. Data were collected from 30 professional human interpreters and three state-of-the-art AI systems (Google Translate Live, DeepL Pro, and a fine-tuned mBART-50 model). Statistical analyses (descriptive statistics, Mann-Whitney U tests, and Kruskal-Wallis H tests) reveal that while AI-SI outperforms H-SI in lexical accuracy (M = 92.4%, SD = 3.1) and temporal alignment (M = 0.87s lag, SD = 0.21), human interpreters demonstrate significantly superior performance in semantic fidelity (M = 86.7% as well as. 71.3%, p < .001) and discourse cohesion (M = 84.1% as well as. 63.9%, p < .001). Error density was significantly lower in human output (Mean Rank = 47.3 as well as. 29.1, p = .002). Findings suggest that current AI systems, despite algorithmic advancements, remain deficient in pragmatic and contextual adaptation core competencies rooted in human cognitive-linguistic processing. The study contributes a validated metric framework for evaluating AI interpreting performance and calls for hybrid human-AI paradigms in professional settings. Implications for interpreter training curricula and machine translation pedagogy are discussed

Keywords: Simultaneous Interpreting، Artificial Intelligence، Deep Learning، Quantitative Descriptive Research، Machine Translation، Human-Machine Comparison، Interpreter Performance، Transformer Model

-This qualitative case study investigates the effectiveness of AI-driven translation technologies in mediating cultural understanding within English Language Teaching (ELT) contexts in Libyan higher education. Drawing on data from semi-structured interviews with 55 ELT instructors, 24 classroom observations across six universities, and document analysis of student translation outputs and institutional curricula, the study reveals a dualistic role of artificial intelligence in language pedagogy. While AI-powered tools such as Google Translate and Microsoft Translator significantly enhance lexical accessibility and reduce language anxiety among learners, their capacity to convey cultural nuance remains critically limited. Findings indicate that algorithmic biases, rooted in Anglo-American linguistic corpora, result in the systematic flattening of cultural meaning and misrepresentation of idioms, humor, politeness strategies, and socio-pragmatic cues, thereby reinforcing a form of digital linguistic imperialism. Students’ uncritical reliance on AI outputs has fostered a “copy-paste culture,” undermining opportunities for intercultural reflection and eroding pragmatic awareness. However, the study also identifies transformative potential when AI tools are pedagogically repurposed through “teaching against the machine” strategies. A subset of instructors successfully used AI-generated mistranslations as pedagogical moments to foster critical cultural awareness, metacognitive reflection, and intercultural dialogue. Despite this potential, widespread implementation is hindered by systemic barriers, including the absence of institutional policies, lack of professional development, curricular gaps, and infrastructural challenges. The research concludes that AI technologies are not inherently facilitative or obstructive to cultural understanding; rather, their effectiveness is contingent upon intentional pedagogical mediation, critical digital literacy, and context-sensitive integration. The study calls for curriculum reforms, ethical AI integration frameworks, and teacher training programs that

position AI as a scaffold, not a substitute for intercultural communicative competence in ELT contexts

KEYWORDS: AI-driven translation, cultural understanding,, English language teaching, higher education, Libya, intercultural competence, technology integration

driven translation technologies in mediating his qualitative case study investigates the effectiveness of AI

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ABSTRACT

This study aimed to explore EFL teachers’ perceptions of the 21st-century PD program in six primary schools in Al Jabal Al Gharbi district. 31 teachers participated in the study. The mixed-research approach was used to conduct the research. A structured questionnaire and a semi-structured interview were used to gather the data. The results revealed that participants acquired several teaching skills, such as new techniques for teaching vocabulary, speaking, and listening language skills, from the 21st-century PD program. Furthermore, the participants reported positive changes in their students’ outcomes, including improved speaking, listening and vocabulary. The findings also revealed some complications in implementing the PD program, including a lack of resources and administrative support at some schools.

This research investigated the challenges medical university students encounter in learning

technical vocabulary. A quantitative research approach was adopted. 61 first-year medical

students at the University of Gharyan participated in the study. A structured questionnaire was

used to gather the data. The findings revealed that medical university students encounter

several challenges in medical vocabulary learning, including pronunciation, spelling, meaning and use. The results also indicated that medical university students struggle to recall some

medical terms, which could be attributed to cognitive overload. The researchers suggest

revaluating the current English medical programs at the Faculties of Medicine in Libya.

Teaching vocabulary in context, highlighting the grammatical categories of words, drawing

distinctions between synonyms through examples, and training students on using medical

words through interactive activities could enhance the current English medical programs.

In the era of digital communication, Facebook has emerged as a powerful platform influencing language use, particularly among university students. This study investigates the phenomenon of "Facebook English," a distinctive digital variety marked by informal grammar, abbreviations, emoticons, and creative lexical formations. Focusing on Libyan English department students, the research explores the relationship between Facebook usage and its impact on academic writing, grammatical accuracy, and perceptions of language formality. Using a quantitative methods approach that includes surveys and statistical analysis, the findings reveal no significant correlation between Facebook use and academic English performance. However, gender and age differences were observed in the use and perception of Facebook English. The study emphasizes the need for language educators to integrate digital literacy and critical language awareness into curricula, enabling students to navigate both informal and academic registers effectively. These findings contribute to the broader understanding of computer-mediated communication and its pedagogical implications in EFL contexts. Keywords: Facebook English, Computer-Mediated Communication (CMC), Sociolinguistics, Libyan University Students, Digital Literacy, Language Variation, Academic Writing, Informal Registers, English as a Foreign Language (EFL), Social Media Linguistics

Abstracts of research articles should be translated from Arabic into English before being submitted for publication in Libyan academic journals. Because English publishing allows non-native speakers to gain international recognition (Duszak & Lewkowicz, 2008). This study aimed to identify the most typical errors made while translating the Arabic abstracts into English. For this purpose, a corpus of 33 translated abstracts of research articles (RA) were analyzed. They were extracted from 11 issues of the Journal of Gharyan University and were published from 2016 to 2021. The analytical frameworks developed by Popescu (2013) and Laio (2010) were adopted. A preliminary analysis was carried out to develop a new category of translation errors that is appropriate for the current corpus. The results revealed a large number of errors in the translated abstracts. They were translation, stylistic, and linguistic errors. It is suggested that the Journal of Gharyan University should devote more effort to evaluating and polishing the translated abstracts before publishing the articles. If they wish to obtain a worldwide reputation, they should also include translators on the journal's editorial board to translate the Arabic abstracts into English appropriately. 

Using Hyland’s (2000) model as a research tool and drawing on Hyland’s (2005a) model of metadiscourse, this article presents a pragmatic two-level rhetorical analysis of the constituent moves within research article abstracts. It specifically zeroes in on the identification and mapping of the most frequently used metadiscourse markers signifying these moves. The findings highlight that Libyan authors employ interactive markers more often than interactional ones. Both quantitative and qualitative analysis of metadiscourse indicate that transitions, endophoric markers, and frame markers emerge as the dominant interactive categories. In contrast, interactional metadiscourse is predominantly represented by attitude markers, hedges, and boosters. Based on the findings, Move 1 features the highest frequency of metadiscourse markers, followed by Move 2. Notably, transitions stand out as the most prevalent category across all moves. This study carries pedagogical implications for academic writing practices among Libyan academic writers and students alike. Moreover, it enhances the existing body of research on the genre of research articles.

This study attempts to identify the use of metadiscourse (MD) in the Libyan science lectures and investigate the lecturers’ perceptions towards the use of MD. Six audio-recorded lectures from the fields of chemistry, physics, and botany were selected in this study. To analyze this collected spoken data, Hyland’s (2005) analytical framework of metadiscourse has been used. The findings showed that interactive metadiscourse was used more than interactional metadiscourse. Among all the subcategories, transitions, engagements and code glosses were the most common markers. The absence and lack of some categories were remarkable as self-mention and evidentials. The results of this study may suggest that when teaching or learning a language, metadiscourse should be given special consideration by both language instructors and students.

Even though abstracts are short, they require a specific type and formal structure that is accepted by academic discourse communities (San & Tan, 2012). They are governed by a discipline's own conventions i.e. a series of moves that characterize the flow of the discourse and linguistic features. This study aims to identify the rhetorical moves and metadiscourse markers that are deployed in the abstracts of projects that were written by undergraduate students in the English department at the faculty of Arts/Gharyan University. The data consisted of 30 abstracts. Hyland's models (2000; 2005) were adopted in this study. The first model (2000) was used to analyze the rhetorical moves. The second one (2005) was employed to analyze the metadiscourse. The findings revealed that most of the abstracts did not consist of the five rhetorical moves. The introduction and purpose moves were prominent, while the method, result, conclusion, and structure (new move) moves were not prominent. The absence of some of the rhetorical moves in the English abstracts of undergraduate projects might make the project abstract unable to achieve its communicative purposes and will lead to difficulty for readers to understand the content of the abstract. For metadiscourse, the findings showed that interactive metadiscourse was deployed more than interactional metadiscourse. Among these two categories, transitions, frame markers, and endophoric were used more frequently.