Abdulrahman Mokbel Mahyoub Hezam

Artificial intelligence (AI) technologies have revolutionized language learning, offering personalized, adaptive, and interactive tools for enhancing vocabulary acquisition. However, implementing AI-driven solutions in conflict-affected regions like Yemen presents unique challenges, including infrastructural constraints, socioeconomic barriers, and the need for cultural relevance. This study explores the effectiveness and obstacles of AI-enhanced vocabulary learning among 75 Yemeni university-level English as a Foreign Language (EFL) students through a mixed-methods approach. The research examines learners’ engagement with AI vocabulary tools, socio-technical barriers impeding adoption, and the comparative effectiveness of AI-mediated versus traditional learning methods. The findings reveal a complex interplay between AI’s potential for improving retention, engagement, and confidence, and the systemic challenges of cost, internet access, and cultural alignment. While AI tools demonstrate promise, their full impact hinges on addressing these barriers through localized solutions, policy interventions, and hybrid models that balance innovation with accessibility. This study contributes to the discourse on AI in education by highlighting the importance of contextually grounded approaches that account for the socioeconomic realities of marginalized learning environments.

This study explores Yemeni EFL students' perceptions of using AI tools in their English language learning, focusing on awareness, frequency of use, and preferred applications. AI-powered tools like ChatGPT, Duolingo, and Grammarly offer personalized, interactive learning experiences that are especially valuable in regions with limited educational resources. The research utilized an online survey distributed via WhatsApp to gather data from Yemeni undergraduate students. Results indicate that while many students are familiar with AI tools and use them regularly for tasks such as vocabulary building and grammar checking, barriers like limited internet access and high AI tool costs remain significant. The study highlights the potential benefits of AI tools in accelerating learning, enhancing autonomy, and providing personalized feedback while addressing the need for improved infrastructure and support in under-resourced areas. The findings provide valuable insights for educators, policymakers, and AI developers to enhance the effectiveness and accessibility of AI tools in language learning, particularly in conflict-affected regions like Yemen.

This paper explores the representation of Yemeni women in selected migration literature, to elucidate the manifold challenges they face due to patterns of male migration. Using Gendered migration theory the study shows how migration narratives portray traditional gender roles within Yemeni households, such as divisions of labour, provider roles and decision-making powers. Through a qualitative analysis of diverse narratives, the research examines how migration impacts and transforms domestic structures and family dynamics, necessitating an increased assumption of caregiving responsibilities and provider roles by the women left behind in Yemen. The texts highlight the economic hardships and strains endured and the resilience and resourcefulness displayed by these women in navigating their new circumstances and adapting to sustain their livelihoods. However, the narratives also reveal various social repercussions of migration, such as enduring family separation, fracturing social networks, and marginalization of women's socially productive functions. By investigating the lived experiences of Yemeni women as depicted in migration literature, this study deepens conceptual understanding of their complex realities and underscores the imperative for empowering women and fostering equitable gender relations within Yemeni social organizations. The findings contribute empirical depth to the existing research on migration studies, gender analyses, and understandings of the social dynamics within Yemeni society.