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

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-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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The collection of comprehensive data is crucial for monitoring antimicrobial resistance (AMR) patterns and implementing targeted interventions. However, there is a lack of data regarding the AMR status of pathogens in Libya, hinders the efforts to address the problem effectively. This retrospective study evaluates the prevalence of Escherichia coli AMR isolates to gentamycin, chloramphenicol, cefoxitin (or) ceftriaxone, meropenem, moxifloxacin (Mox), amoxicillin/clavulanate (AUG) or ampicillin-sulbactam (UNZ) among the visitors to the main pathology laboratories in Gharyan city. Reports from 107 AMR tests demonstrate that E. coli was mostly isolated from Urinary Tract Infections (UTIs, 85%) followed by vaginal infections (8.4%) and wound infections (6.5%). More than half of the isolates were multi-drug resistant which is a clear indicator of the need for continuous monitoring. The overall pattern demonstrates a high resistance rate to cephalosporins, and combinations of penicillins with beta-lactamase inhibitors (UNZ or AUG), followed by high resistance rates to Mox. In contrast, E. coli isolates showed high sensitivity to meropenem followed by gentamicin and chloramphenicol. However, the isolation site seems to affect the resistance patterns, most of the wound isolates were resistant to gentamycin. In contrast, all vaginal isolates were resistant to UNZ or AUG, yet both types showed high susceptibility to meropenem. 

Spiders are widespread, abundant predators, making them familiar and readily accessible to people everywhere. Despite having venom glands, most spiders bite humans in some situations, only when they are annoyed, trapped or injured. Studies on spider bites in Libya are limited, neglected, and affected by misdiagnosis and underreporting. The study aimed to interpret, analyze, and characterize the clinical characteristics of spider bite patients and their variations at Tripoli Medical Center (TMC), Tripoli, Libya, between 2004 and 2012. A retrospective study of 102 reported patients with spider bites at Tripoli Medical Center during the period from 2004 to 2012. A total of 102 patients were reported, mostly in May and June (16 cases in each month). The cases were mostly reported in 2007 and 2009 (17 each). The majority of cases were in age group of 19-29 years (41 cases), and female cases (68 cases, 66.6%) were higher than males (34 cases, 33.3%). The most bitten sites were proximal extremities (71.5%). Overall, most patients suffered from erythema (90%) and swelling (36%), followed by itching (33%), necrosis (29%) and fever (17%). Spider bites are common in Libya, but most bites cause minimal or no clinical effects and do not require medical attention. Diagnosing a spider bite is extremely challenging, but a comprehensive history and careful evaluation of symptoms, with correct verification of spider bites are helpful in diagnosing and treating spider bites.