Articles

Factors Influencing English Teachers’ Use of Generative Ai for Teaching Speaking Activities at a Language Center in Ca Mau

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Despite the potential of Generative AI (GenAI) to reduce Foreign Language Anxiety (FLA) in speaking instruction, its implementation in resource-constrained settings like a language center in Ca Mau province, Vietnam, remains underexplored. This study investigates the paradox of “digital native” instructors who possess high digital literacy but face infrastructural and pedagogical barriers in the Mekong Delta region of Vietnam. Employing an explanatory sequential mixed-methods design, quantitative data were collected from 58 EFL teachers via surveys, followed by semi-structured interviews. Utilizing the SAMR model as a diagnostic framework, the findings revealed the existence of a Substitution Plateau. Although teachers frequently used GenAI for administrative tasks (Substitution level, M = 3.91), its application for transformative, real-time voice interactions was notably limited (Redefinition level, M = 1.96). Stepwise multiple regression and thematic analyses demonstrated that infrastructural barriers (beta = -.52) and ethical concerns regarding “synthetic fluency” significantly inhibited advanced GenAI adoption, outweighing the positive influence of teachers’ TPACK competence. The study concludes that in peripheral educational settings, the primary limiting factor is not technological illiteracy, but rather a contextually driven “safety-first” pedagogical strategy. These findings challenge the universality of tech-integration models and provide localized implications for AI adoption in the Global South.

Generative AI in the Categorisation of Paediatric Pneumonia on Chest Radiographs

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Paediatric pneumonia is a leading cause of morbidity and mortality worldwide, necessitating accurate and timely diagnosis. This study explores the application of Generative AI for categorising paediatric pneumonia using chest radiographs. Leveraging deep learning techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we enhance image quality, generate synthetic training data, and improve model generalizability. The proposed framework integrates AI-driven feature extraction, convolutional neural networks (CNNs), and attention mechanisms to improve diagnostic accuracy. The results demonstrate significant improvements in classification performance compared to traditional methods, with a focus on interpretability and clinical usability.