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From Awareness to Practice: Exploring the Knowledge, Attitudes, and Practices of Secondary ESL Teachers in the Philippines Toward ChatGPT in Education
Get PDFThe rise of generative artificial intelligence (AI), particularly ChatGPT, has brought significant changes to educational practice. While research has largely emphasized student use, the perspectives of teachers, especially those in English as a second language (ESL) instruction, remain limited. This study examined the knowledge, attitudes, and practices (KAP) of 181 Filipino secondary ESL teachers in Zamboanga City regarding ChatGPT integration in language teaching. Using a descriptive-comparative quantitative design, data were gathered through the validated KAP-CQ39 instrument and analyzed via SPSS. The findings revealed that participants demonstrated a moderate level of knowledge, a somewhat positive attitude, and high positive usage of ChatGPT. Gender-based comparisons revealed no significant differences across the KAP dimensions. The item-level analysis highlighted the uneven awareness of ChatGPT’s features, ethical implications, and varied implementation in classroom settings. These findings suggest a growing interest among ESL educators in engaging with AI tools, although knowledge gaps and ethical uncertainties persist. The study highlights the need for targeted training, institutional support, and clear guidelines to foster the responsible and effective use of ChatGPT in language education. This study contributes to a deeper understanding of AI adoption in linguistically diverse educational contexts within the Philippine context
Generative artificial intelligence in tourism: current status and emerging trends
Get PDFIntroduction: Generative Artificial Intelligence (GAI) has consolidated in recent years as a disruptive technology with the capacity to transform the tourism sector through experience personalization, content generation, and process optimization. Methods: A bibliometric approach was applied to analyze the scientific literature on GAI in tourism. Data were retrieved from the Scopus database, considering publications between 2020 and 2025. Records were processed using the Biblioshiny tool. Indicators evaluated included annual scientific production, sources, institutions, keywords, collaboration networks, and thematic maps. Results: A total of 412 documents were identified. Scientific production grew exponentially from 2022 onward, reaching over 150 articles in 2023 and 180 in 2024. Current Issues in Tourism and International Journal of Hospitality Management were the most productive journals. The University of Macau led institutional output, while China and the United States were the countries with the highest levels of international collaboration. Thematic maps revealed consolidated, specialized, and emerging topics. Conclusions: GAI in tourism is a rapidly expanding and multidisciplinary field, with notable geographic and institutional concentration. The findings provide a foundation for guiding research strategies, technological investment, and training, highlighting the need to integrate ethical and sustainability dimensions into its future development
Incorporating AI-Generated Duolingo within Collaborative SLL: Spoken English Students at FLDM-USMBA as a case study
Get PDFIn today’s digital age, the combination of AI-generated learning platforms and Second Language Learning (SLL) has revolutionized the way students learn new languages, mostly because these tools provide individuals with personalized learning content that is actively adjusted and tailored so that students can dynamically enhance their language skills according to their strengths, weaknesses, and progress. Thus, the prospect of such tools expands beyond self-paced learning and creates considerable opportunities for improvement in collaborative language learning settings. This article explores the integration of Duolingo into group-based learning contexts, focusing on its potential to enhance collaborative Second Language Learning (SLL) through its gamified structure and community features such as leaderboards, clubs, and challenges. The study adopts a mixed-methods exploratory design and was conducted among 189 Bachelors (BA) students enrolled in Spoken English (SE) classes during the 2024-2025 academic year at Sidi Mohamed Ben Abdellah University of Fes, Morocco (USMBA). Being a case study, the study investigates how Duolingo; as an AI-generated Language Learning Tool, and how its collaboration-focused features influence students’ motivation, engagement, and communication skills within a collaborative SLL framework. The study argues that when used alongside traditional classroom methods, Duolingo serves as a powerful tool for promoting both individual and group-based language acquisition, thereby enhancing the overall learning experience
Interpretable AI for Behavioral Prediction: An Ethical Laboratory Experiment on Snack Choice Prediction
Get PDFIntroduction: The application of artificial intelligence (AI) in behavioral prediction has shown promise across domains like mental health, autonomous vehicles, and consumer behavior. However, challenges such as algorithmic bias, lack of interpretability, and ethical concerns persist. This study addresses these gaps by developing an interpretable AI model to predict snack choices in a controlled laboratory experiment.Methods: A random forest classifier was trained to predict participants’ snack choices (healthy vs. unhealthy) based on contextual factors (hunger, mood, time of day) and historical choices. Data were collected from 75 adults over 10 sessions, with features engineered to capture both immediate and longitudinal patterns. Model performance was evaluated using accuracy, precision, recall, and feature importance analysis.Results: The model achieved 85.33% accuracy, with hunger level, historical choices, and mood identified as the most influential predictors. Performance improved over sessions (peaking at 93.33% accuracy in sessions 8–9), highlighting the value of longitudinal data. Subgroup analyses showed consistent performance across age, gender, and BMI, with higher accuracy for participants with healthier habits and higher socioeconomic status.Conclusions: This study demonstrates the feasibility of interpretable AI models in predicting dietary behavior while addressing ethical concerns through rigorous data anonymization and informed consent protocols. The findings underscore the potential of AI to inform personalized interventions for healthier eating habits and provide a framework for ethical AI implementation in behavioral research
Role of Artificial Intelligence in Cross-sectional Studies in Rural India: Prospects, Obstacles, and Future Directions
Get PDFCross-sectional studies are critical as sources of the health, socio-economic, and demographic dynamics of rural populations in India. However, these studies suffer from some drawbacks, including logistics issues, data validity, and limited funding. Recent advances in AI have demonstrated the possibility of enhancing various aspects of cross-sectional study design, data acquisition, and statistical and interpretational methods. This manuscript outlines how AI can complement cross-sectional studies in rural India, describes the challenges of AI implementation, and envisions ways in which AI options may be incorporated into future rural health research.
Global Digital Transformation: Ensuring the Protection of IP Rights
Get PDFToday, intellectual property (IP) is a key global development driver. Its institution forms the basis of the economy, its virtually inexhaustible resource. Against the backdrop of large-scale and rapid digitalisation of public life, intellectual property is acquiring the functions of a toolkit for forming an up-to-date digital market, which requires a study of the transformation of the IP institution. The article aims to analyse the key trends in developing the system of intellectual property rights protection against the background of the digitalisation of global society. It examines the functionality of IP in the new digital era and outlines the main related risks and challenges. The study finds that rapid informatisation has become a key cause of large-scale infringements of IP rights. It examines modern innovative technologies and effective approaches in the practical experience of developed countries in protecting intellectual property rights. The article analyses modern scholars\u27 positions regarding assessing the current level of IP rights protection. It highlights the need to integrate advanced digital technologies in terms of the IP protection strategy and identifies and analyses the most effective ones. The study establishes that this process may require separate targeted measures within the legislative and legal regulation framework. It has been proved that this problem should be addressed through a comprehensive global upgrade of IP legislation to introduce and strengthen a generally favourable legal regime that considers innovation trends to the maximum extent possible. It is substantiated that today, upgrading traditional legal approaches to protecting intellectual property rights is necessary.
Publishing Ethics and AI: Challenges and Opportunities for Algorithm-Generated Content
Get PDFIntroduction: the adoption of artificial intelligence (AI) has changed the content production process, and the AI generated text, images, and other media have contributed to controversies regarding ethical and operational aspects that publishers, authors, and users still struggle to address.Objective: the study under consideration examines both opportunities and challenges of publishing the work of AI-generated content, with a particular focus on its operational aspects, quality control, and other ethical considerations.Method: a survey was carried out among 874 participants, such as content creators, editors, and most frequent readers, who are asked to provide their perceptions, trustworthiness, and engagement patterns in an algorithm-generated content. Convergent validity and internal consistency tests were taken to determine the validity and reliability of the constructs to ensure that there is accuracy in measurements and the model is resilient. Analytical procedures employed structural equation modeling (SEM) using SmartPLS, supplemented with regression and correlation analyses in IBM SPSS (Version 29.0), to examine relationships between AI utilization, ethical awareness, content quality, and reader trust.Results: key challenges include AI bias, disinformation, and low accountability, while opportunities involve efficiency, scalability, and personalized experiences. The strongest correlation is between transparency and reader trust (r = 0,63). Regression shows Ethical Awareness as the top predictor (β = 0,49, p < 0,001), and SEM path analysis identifies Ethical Awareness Perceived Content Quality as the strongest path (β = 0,54, p < 0,001). Transparency increases trust (β = 0,42, p < 0,01), perceived bias reduces credibility (β = –0,36, p < 0,01), and ethical supervision (human-in-the-loop, audits) enhances engagement and reliability.Conclusions: the results indicate that a set of ethical principles and adjusted governance frameworks are needed in publishing to make AI-generated content creative and ethically reasonable
AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance
Get PDFThe increasing complexity of global air traffic management requires innovative surveillance solutions beyond traditional radar. This chapter explores the integration of artificial intelligence (AI) and machine learning (ML) in satellite imagery processing for enhanced air traffic surveillance. The proposed AI framework utilizes satellite remote sensing, computer vision algorithms, and geo-stamped aircraft data to improve real-time detection and classification. It addresses limitations in conventional systems, particularly in areas lacking radar coverage. The study outlines a three-phase approach: extracting radar coverage from satellite imagery, labeling data with geo-stamped aircraft locations, and applying deep learning models for classification. YOLO and Faster R-CNN models distinguish aircraft from other objects with high accuracy. Experimental trials demonstrate AI-enhanced satellite monitoring\u27s feasibility, achieving improved detection in high-traffic zones. The system enhances situational awareness, optimizes flight planning, reduces airspace congestion, and strengthens security. It also aids disaster response by enabling rapid search-and-rescue missions. Challenges like adverse weather and nighttime monitoring remain, requiring infrared sensors and radar-based techniques. By combining big data analytics, cloud computing, and satellite monitoring, the study offers a scalable, cost-effective solution for future air traffic management. Future research will refine models and expand predictive analytics for autonomous surveillance, revolutionizing aviation safety and operational intelligence
Robust Face Tracking Under Challenging Conditions Using Linear Regression and YOLO algorithm
Get PDFFace detection and tracking play a crucial role in various computer vision applications, including surveillance, fault face detection systems, artificial intelligence, etc. The objective of this paper is to enhance the precision of face detection and tracking through the introduction of an innovative approach centered on the linear regression algorithm. The effectiveness of the proposed method was compared to the traditional Kalman filter approach. Additionally, the study explored the integration of the YOLO algorithm for face detection with the linear regression tracking algorithm to further enhance accuracy. The proposed algorithm\u27s performance is assessed through comprehensive experiments on annotated images and video sequences affected by occlusions or other issues such as poor lighting conditions and motion blur. These experiments utilize the COCO dataset, operating at a speed of 60 FPS. The experimental results show that the proposed method can accurately track the human face in different facial position
AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction
Get PDFBackground: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental health and overall well-being. Artificial intelligence (AI) offers innovative approaches to predicting and mitigating excessive internet use.
Objective: This study aims to develop and evaluate AI-driven machine learning models for predicting and mitigating internet addiction by analyzing behavioral patterns and psychological indicators.
Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” and “Social Media Usage and Mental Health”, were analyzed using machine learning and deep learning models, including Random Forest, XGBoost, Neural Networks, and Natural Language Processing (NLP) techniques. Model performance was assessed based on accuracy, precision, recall, F1-score, and AUC-ROC.
Results: Neural Networks and XGBoost achieved the highest accuracy (91% and 90%, respectively), surpassing traditional models like Logistic Regression and SVM. Clustering and anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional and thematic patterns associated with addiction.
Conclusion: AI-driven models effectively predict and classify internet addiction, offering scalable and personalized interventions to promote digital well-being. Future research should focus on addressing ethical concerns and improving real-time deployment of these models