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Integration of ChatGPT in the Translation and Post-Editing of Specialized Texts: A Study on its Application
Get PDFThis study examines the impact of artificial intelligence (AI) on specialized translation, using ChatGPT as the primary tool. Employing an empirical-exploratory and mixed-methods approach, it analyzes the strategies and translation skills of 15 advanced students from the Translation Bachelor’s Program at UABC while translating specialized texts from legal, medical, and scientific fields. It also describes the post-editing process and techniques used to enhance terminological accuracy and cultural appropriateness. Participants translated and post-edited three specialized texts, complementing the process with the creation of terminological glossaries. A specialized rubric was used to evaluate translation quality, while Translog-II software measured efficiency and time spent on each task. The objectives were to analyze the translation process and strategies employed with ChatGPT in translating specialized texts, and to describe the post-editing techniques used by students in their final academic stage. Preliminary results show a significant improvement in the efficiency and quality of translations due to the use of AI tools, highlighting the positive impact of ChatGPT on the development of specific translation competencies. Students expressed a favorable perception of the experience, emphasizing the usefulness of these tools in facilitating and optimizing the translation proces
AI-assisted abnormal CXR findings and correlation with behavioral risk factors: A Public Health Radiography approach to formulating policies and effective interventions
Get PDFIntroduction: Cardiovascular, respiratory and related diseases (CVRDs) constitute over 40% cause of death worldwide, mostly reported in low-and-middle-income countries. The catastrophic effect of this spans across poor health outcomes, severe economic loss and significant societal consequences. Responding to this situation necessitates collective strategy to prevent further deterioration as these conditions are closely related, share common risk factors as well as control measures at the clinical, population and policy levels. Thus, this study is aimed at understanding the distribution of AI-assisted abnormal adult chest X-ray (CXR) and examine relationship with behavioral factors; to lay foundation for planned interventions. Methods: Prospective mixed-methods research, cross-sectional in nature, conducted across six top-rated hospitals in Nigeria, representing the six geopolitical zones of the country via purposive sampling technique. Quantitative aspect involved data collection on demographics and abnormal findings from AI-assisted technology, while Qualitative aspect explored individual’s behavioral choices in relation to risk factors. Informed consent and ethical approval were obtained; SPSS software utilized for descriptive and correlation analysis. Results: Cardiomegaly(15.35%), pleural effusion(14.03%), fibrous opacities(10.43%), pleural capping(8.51%), pulmonary mass(7.91%), apical opacities(7.55%), consolidation(6.59%), infiltration(5.88%) among the sixteen abnormal findings in decreasing order of magnitude. An early onset of these anomalies at 30 years was noted, hitting peak values at 40-44 years. A significant percentage of the population engages in unhealthy lifestyle, found to positively correlate with these anomalies in varying degrees; low education levels, health education gaps, poor income and environmental challenges clearly seen. Conclusion: A Public Health Radiography approach- AI assisted, engaging with empirical evidence provides a novel and valuable strategy in designing effective interventions and policy making to address CVRDs burden.
The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection
Get PDFEarly warning systems (EWS) are crucial for disaster risk reduction, providing timely and reliable information to communities and authorities for proactive mitigation. Traditional methods, such as weather stations, river gauges, and seismic networks, have limitations in spatial coverage, real-time data availability, and precursor signal detection. Recent technological advancements have enhanced EWS by integrating remote sensing data from satellites, airborne platforms, and ground-based sensors, enabling real-time monitoring of phenomena like wildfires, volcanic activity, and landslides. The Internet of Things (IoT) and crowdsourced data from social media, mobile apps, and citizen reports have further improved situational awareness and response times, complementing traditional systems. Increased computational power has enabled the development of sophisticated models, such as numerical weather prediction and seismic hazard models, which predict disaster impacts more accurately. Despite these advancements, challenges remain in data interoperability, resilient communication infrastructure, and delivering clear, actionable alerts to at-risk populations. Future EWS will likely become more data-driven and interconnected, leveraging artificial intelligence, big data analytics, and IoT. Collaboration among governments, academic institutions, and local communities is essential to building robust, inclusive EWS that save lives and reduce the economic impact of disasters
Article Context and Technological Integration: AI\u27s Role in Climate Change Research
Get PDFThis article explores the transformative role of artificial intelligence and machine learning in tackling climate change. It highlights how advanced computational techniques enhance our understanding and response to environmental shifts. Machine learning algorithms process vast climate datasets, revealing patterns that traditional methods might overlook. Deep learning neural networks, particularly effective in climate research, analyze satellite imagery, climate sensor data, and environmental indicators with unprecedented accuracy. Key applications include predictive modeling of climate change impacts. Using convolutional and recurrent neural networks, researchers generate high-resolution projections of temperature rises, sea-level changes, and extreme weather events with remarkable precision. AI also plays a vital role in data integration, synthesizing satellite observations, ground-based measurements, and historical records to create more reliable climate models. Additionally, deep learning algorithms enable real-time environmental monitoring, tracking changes like deforestation, ice cap melting, and ecosystem shifts. The article also highlights AI-powered optimization models in mitigation efforts. These models enhance carbon reduction strategies, optimize renewable energy use, and support sustainable urban planning. By leveraging machine learning, the research demonstrates how AI-driven approaches offer data-backed solutions for climate change mitigation and adaptation. These innovations provide practical strategies to address global environmental challenges effectively
Transforming Supply Chain Finance with AI and IoT for Greater Inclusivity, Efficiency, and Intelligence
Get PDFThe integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing supply chain finance by making it more inclusive, efficient, and intelligent. AI-driven algorithms automate critical financial processes such as credit analysis, risk management, and fraud detection, while IoT-enabled devices provide real-time visibility into inventory and asset tracking. These technologies streamline operations, enhance transparency, and enable dynamic, data-driven decision-making. Additionally, AI and IoT solutions democratize access to financing, particularly for small and medium enterprises (SMEs), by leveraging real-time data to assess creditworthiness. This paper explores how the fusion of AI and IoT is transforming supply chain finance, offering innovative strategies for improved efficiency, risk reduction, and financial inclusion
Artificial Intelligence Anxiety and Attitudes among Pre-Service and In-Service Physical Education Teachers: Addressing an Underserved Field in AI Education
Get PDFTeachers’ attitudes and anxiety toward Artificial Intelligence (AI) play a crucial role in shaping how AI is adopted in Physical Education (PE) settings. This study aimed to compare the attitudes and anxiety levels of pre-service and in-service PE teachers and to examine the relationships among these variables. Using a descriptive–correlational design, data were gathered from 200 participants (100 pre-service and 100 in-service) through two standardized instruments: the General Attitudes toward Artificial Intelligence Scale (GAAIS) and the Artificial Intelligence Anxiety Scale (AIAS). Results showed that teachers held moderately positive attitudes toward AI (M = 3,28, SD = 0,67) and experienced a moderate level of anxiety (M = 4,31, SD = 1,21). Among the four anxiety domains, Sociotechnical Blindness and Job Replacement recorded the highest means, reflecting concerns about AI misuse, malfunction, and potential job displacement. In-service teachers demonstrated slightly higher anxiety than pre-service teachers (r = ,181, p = ,010). Correlational analysis showed a weak positive relationship between teacher status and AI anxiety (r = ,181, p = ,010), a strong negative correlation between AI anxiety and negative attitude (r = –,512, p < .001), and a moderate positive correlation between AI anxiety and positive attitude (r = ,235, p < ,001).These findings suggest that PE teachers are cautiously optimistic about AI’s instructional potential while remaining aware of its ethical and occupational risks. Strengthening AI literacy, ethical training, and professional development is recommended to promote confident and responsible AI integration in physical education.
Pre-Service and In-Service Teachers’ Perspectives on Artificial Intelligence in Education: Insights from Physical Education and Classroom Practice in the Southern Philippines
Get PDFArtificial intelligence (AI) is increasingly shaping educational practice, yet teachers’ attitudes remain divided, combining optimism about its benefits with apprehension about risks. Limited research has compared preservice and in-service teachers’ perspectives, particularly in the context of physical education (PE), where AI applications such as sports analytics, performance monitoring, and adaptive training are emerging. This study aimed to compare the attitudes of preservice and in-service teachers toward AI in education and to examine differences when they are grouped according to gender and socioeconomic status. A descriptive-comparative quantitative design was employed with 400 participants, comprising 200 preservice PE students preparing to become future teachers and 200 in-service teachers in public schools in the southern Philippines. Data were collected via a standardized survey measuring positive and negative attitudes toward AI, and the results were analyzed via weighted means, independent samples t tests, and one-way ANOVA. The overall mean of 3,11 indicated a neutral attitude toward AI. The respondents expressed positive views of AI’s potential to create economic opportunities, support well-being, and offer beneficial applications but also concerns about errors, ethical misuse, surveillance, and control. No significant gender differences were found, although moderate to large effect sizes suggested subtle variations. Socioeconomic status did not influence preservice teachers’ responses, but in-service teachers from higher-income groups reported stronger negative attitudes. A significant difference was observed between groups: preservice PE students demonstrated more positive attitudes, whereas in-service teachers expressed greater reservations. These findings highlight the need to embed AI literacy in PE curricula, strengthen professional development for in-service teachers, and promote equitable access to AI resources to ensure the balanced and responsible adoption of AI in education
Automation of Production Management Processes Using Artificial Intelligence: Impact on the Efficiency and Resilience of Manufacturing Systems
Get PDFThe rapid technological advancement and global competition provokes the automation of production management processes through artificial intelligence. This study investigates the integration of artificial intelligence into production management and its influence on the efficiency and resilience of manufacturing systems. The research is motivated by the growing relevance of AI within the paradigm of Industry 4.0, where advanced digital technologies are transforming traditional production models. The main objective is assessing how AI technologies – such as machine learning, deep learning, predictive analytics, and intelligent automation – enhance core production functions, including planning, quality control, maintenance, logistics, and energy management. The study applies a mixed-method approach, combining comparative analysis, case study evaluation, and content analysis of scientific and industrial data. Empirical evidence (1653 records) was drawn from both international (e.g., Siemens, Fanuc, Bosch) and Ukrainian (e.g., Interpipe, Kernel) manufacturing companies. Results after screening, filtration, validation, verification and exclusion (50 records) demonstrate measurable improvements in key performance indicators, such as reduced downtime, decreased defect rates, increased logistical accuracy, and optimized energy use. At the same time, the paper addresses the challenges accompanying AI integration, including cybersecurity risks, social impacts, regulatory gaps, and organizational readiness. The research concludes that AI not only improves operational performance but also strengthens adaptive capacity and strategic stability, contributing to the formation of intelligent, self-learning, and data-driven production systems. This article will be of particular interest to production managers, industrial engineers, innovation strategists, policymakers, and academic researchers seeking to understand and apply AI for sustainable industrial transformation.
Attitude, Acceptability, and Perceived Effectiveness of Artificial Intelligence in Education: A Quantitative Cross-sectional Study among Future Teachers
Get PDFThis study investigated the extent of prospective teachers’ acceptance, attitudes, and perceived effectiveness of artificial intelligence (AI) in education. It also examined whether these perceptions varied according to gender and age group. Using a descriptive-correlational design, data were gathered from 392 teacher education students enrolled in a state-managed university in southwestern Mindanao. The results revealed that the respondents generally demonstrated moderate acceptance, favorable attitudes, and positive perceptions of AI effectiveness in the teaching and learning process. While no statistically significant differences were found between genders, moderate effect sizes suggested subtle variations worth further exploration. Significant differences were observed across age groups, with older individuals reporting higher levels of AI acceptance. Strong and significant correlations among acceptance, attitude, and perceived effectiveness affirmed the interconnected nature of belief, emotion, and evaluation in shaping readiness for AI integration. These findings support the Technology Acceptance Model and the Theory of Planned Behavior. In light of these results, it is recommended that teacher education programs integrate AI literacy and practical training, with targeted support for younger students to enhance digital confidence and preparedness
Ethical and Privacy Considerations in AI-Driven Language Learning
Get PDFArtificial intelligence (AI) has revolutionized language learning by enabling personalized and adaptive education; however, these advancements also raise ethical and privacy concerns, including algorithmic bias, data security risks, and a lack of transparency in AI-driven decision-making. This study examines these challenges, focusing on fairness, linguistic diversity, and the balance between automated and human instruction, with the goal of proposing ethical guidelines for the responsible adoption of AI in language education. Through a literature review and comparative analysis, ethical and privacy risks in AI-powered language learning tools were explored, assessing bias detection algorithms, transparency frameworks, and privacy-preserving techniques to identify best practices. The findings indicate that AI-driven language tools tend to exhibit biases that disadvantage underrepresented linguistic groups, raising concerns about fairness while also exposing privacy risks due to inadequate security measures. Implementing ethical AI frameworks that incorporate fairness-aware algorithms, explainable AI models, and robust data protection mechanisms enhances user trust and security. Therefore, addressing these issues is essential for ensuring the ethical integration of AI in language education, where a hybrid approach combining AI with human instruction emerges as the most responsible solution. Lastly, future research should focus on regulatory compliance and adaptive learning models to strengthen AI ethics in education.