The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
Machine Learning-Based and AI Powered Satellite Imagery Processing for Global Air Traffic Surveillance Systems
The unprecedented growth of global air traffic has put immense pressure on the air traffic management systems. In light of that, global air traffic situational awareness and surveillance are indispensable, especially for satellite-based aircraft tracking systems. There has been some crucial development in the field; however, every major player in this arena relies on a single proprietary, non-transparent data feed. This is where this chapter differentiates itself. AIS data has been gaining traction recently for the same purpose and has matured considerably over the past decade; however, satellite-based communication service providers have failed to instrument significant portions of the world’s oceans. This study proposes a multimodal artificial intelligence-powered algorithm to boost the estimates of global air traffic situational awareness using the Global Air Traffic Visualization dataset. Two multimodal artificial intelligence agents categorically detect air traffic streaks in a huge collection of satellite images and notify the geospatial temporal statistical agent whenever both modalities are in concordance. A user can fine-tune the multimodal threshold hyperparameter based on the installed detection rate of datasets to get the best satellite-derived air traffic estimates.
Copyright (c) 2025 Fredrick Kayusi, Petros Chavula, Linety Juma, Rashmi Mishra (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Anxiety and Attitudes among Pre-Service and In-Service Physical Education Teachers: Addressing an Underserved Field in AI Education
Teachers’ 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.
Copyright (c) 2025 Keith Alvin Solamillo (Author) https://creativecommons.org/licenses/by/4.0
Publishing Ethics and AI: Challenges and Opportunities for Algorithm-Generated Content
Introduction: 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.
Copyright (c) 2025 Zonghui Wu, Yongguo Hu, Li Dai, Wenjie Huang, Jie Liu, Yahui Li, Yongsong Yan (Author) https://creativecommons.org/licenses/by/4.0
AI Integration in Education: A Correlational Study on Attitudes, Perceptions and Anxiety Among Pre-Service Teachers
This study investigated pre-service teachers’ attitudes, perceptions, and anxiety toward artificial intelligence (AI), with gender, age, and socioeconomic status (SES) as demographic factors. Using a descriptive-quantitative correlational design, data were collected through an online survey from 150 valid respondents. Attitude was measured using the General Attitudes toward AI Scale, perception through an adapted 38-item instrument, and anxiety through the AI Anxiety Scale with four subscales. Descriptive statistics summarized levels, while t-tests, ANOVA, and Pearson correlation tested differences and relationships among variables. Results showed that respondents exhibited a generally positive attitude on favorable statements (M = 3,40), but expressed reservations on negative items (M = 2,49). Their overall perception was neutral (M = 2,68), while AI-related anxiety was moderate (M = 4,40), with higher levels in job replacement and sociotechnical blindness. Gender differences were not significant for attitude and perception, but female respondents reported significantly higher anxiety than males (p = ,010, large effect). No significant differences were observed across age groups, while SES revealed no variation in attitude and perception but showed significant differences in anxiety (p = ,026), with middle-class and poor respondents scoring higher than low-income groups. Correlation analysis indicated a moderate positive relationship between perception and anxiety (r = ,464, p < ,001), while attitude showed weak and nonsignificant links with both. Overall, the findings suggest cautious openness to AI among pre-service teachers, underscoring the need for teacher education programs to integrate AI-focused training and ethical discourse to reduce anxiety and enhance readiness for responsible AI integration.
Copyright (c) 2025 Aprillette C. Devanadera (Author) https://creativecommons.org/licenses/by/4.0
Research Trends and Collaboration Patterns in Quantum Internet: A Bibliometric Mapping Using Biblioshiny, VOSviewer, and CiteSpace
Introduction Quantum Internet has emerged as a transformative technological frontier, attracting increasing global research attention. Its development spans foundational physics, cryptography, network engineering, and applied technologies, demanding systematic evaluation of scholarly progress. Objective This study aims to present a comprehensive bibliometric analysis of Quantum Internet research, identifying publication trends, influential contributors, collaborative networks, and evolving thematic directions. Method Data were retrieved from the Scopus database and analyzed using Biblioshiny, VOSviewer, and CiteSpace. The study examined publication growth, author and journal influence, country-level collaborations, bibliographic coupling, keyword co-occurrence, and thematic evolution. Results Findings indicate steady growth in publications since 2016, peaking in 2024 with 130 documents. Leading contributors include Angela Sara Cacciapuoti, Marcello Caleffi, Laszlo Gyongyosi, and Stephanie Wehner, while prominent publication outlets are Physical Review Letters, Physical Review A, Scientific Reports, and New Journal of Physics. Country-level analysis highlights the United States, China, the United Kingdom, Italy, and Japan as primary research hubs, with strong international collaborations. Network analyses reveal 15 author and 10 journal clusters, underscoring interdisciplinary connections. Bibliographic coupling and keyword co-occurrence identify critical themes such as quantum key distribution, entanglement distribution, quantum memory, blockchain, and cybersecurity, alongside emerging areas like quantum IoT and quantum machine learning. Thematic maps show a shift from theoretical constructs to application-driven studies, integrating artificial intelligence and advanced sensing. Trend analyses confirm growing attention to scalability, security, and interdisciplinary applications, though gaps remain in resource optimization, experimental validation, and integration with classical infrastructures. Conclusions This analysis provides a knowledge framework and practical insights into the intellectual and conceptual structure of Quantum Internet research. By mapping influential contributors, core themes, and research gaps, the study supports academics, policymakers, and industry in making strategic investments and advancing future research in this rapidly evolving field.
Copyright (c) 2025 Annu Paul, Jobin Varghese P, Rejina P V, Paulin Paul, Anat Suman Jose, Jobin Jose (Author) https://creativecommons.org/licenses/by/4.0
Automation of Production Management Processes Using Artificial Intelligence: Impact on the Efficiency and Resilience of Manufacturing Systems
The 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.
Copyright (c) 2025 Kateryna Kolos , Oleg Kubrak , Yuliya Olimpiyeva, Pavlo Ihnatenko , Olena Furtat (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence tools for automating philological text research
Introduction: in recent years, artificial intelligence (AI) has significantly advanced across various fields, including linguistics, particularly in translation. While AI offers substantial opportunities for translation automation, scholarly debates continue regarding its reliability and impact on the translation process. Purpose: This study aims to analyze the role of artificial intelligence in automating linguistic research and to provide a comprehensive evaluation of its main advantages and limitations compared to human translation. Method: The study employs a systematic literature review based on scientific articles from Google Scholar, ResearchGate, and Scopus. A total of 55 scientific articles were analyzed, 25 specifically focused on automated translation's characteristics and challenges. Results: The findings indicate that collaboration between human translators and artificial intelligence is the most effective approach. AI is an auxiliary tool that enhances translation efficiency but cannot fully replace human translators due to their unique ability to convey emotions, cultural subtleties, and linguistic nuances. AI has great potential in overcoming language barriers but remains limited in comprehending cultural context and stylistic intricacies. Conclusion: The optimal use of AI in translation is in cooperation with human translators. While AI can significantly augment translation efficiency, it cannot entirely replace human expertise, particularly in literary and academic texts.
Artificial Intelligence as a Support Tool
Copyright (c) 2025 Ivan Bakhov, Nataliia Ishchuk, Iryna Hrachova, Liliana Dzhydzhora, Iryna Strashko (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence as a Pedagogical Resource in Initial Teacher Training
This study examines the integration of Artificial Intelligence (AI) in initial teacher education, focusing on its role in strengthening classroom curriculum management through teaching and assessment methodologies. A quantitative and descriptive design was applied using a Likert-type scale, with confirmatory factor analysis for validation and Cronbach’s alpha for reliability, complemented by inferential analysis. Findings indicate that teaching how to teach with AI is shaped by classroom practices, influencing how AI-based processes are perceived and their alignment with competency achievement. Results suggest that pre-service teachers develop greater confidence in using AI for didactic design, although only a small percentage view AI’s organization and presentation as fully coherent and useful for verification and continuous improvement.
Copyright (c) 2025 Melvin Octavio Fiallos Gonzales , Leocadio Fiallos Gonzales (Author) https://creativecommons.org/licenses/by/4.0
Implementation of Artificial Intelligence Systems in Public Administration in Latin America: Impacts and Challenges
Introduction: an analysis of scientific production on the implementation of artificial intelligence in public administration in Latin America during the 2020-2024 period provides insight into the trends and areas of greatest impact. This field has experienced significant growth in 2021 and 2023, reflecting the academic and public interest in the challenges and opportunities that AI presents in the public sector. Methods: a bibliometric review of scientific publications related to artificial intelligence in public administration was conducted, considering the temporal, geographic, and thematic distribution of articles indexed in international academic databases. Results: Brazil (18 publications), Mexico (12 publications), and Colombia (10 publications) are the leading countries in Latin America regarding AI implementation research. The most frequent topics (accounting for 62% of publications) address operational efficiency, digital governance, transparency, and citizen engagement. Qualitative findings indicate that AI adoption improves decision-making and process automation but faces persistent challenges, including ethical considerations (reported in 45% of studies), data privacy issues (38%), and limited technical capacity (33%). Conclusions: the overview highlights the complexity and diversity of approaches adopted to study artificial intelligence in the public sector. It also highlights the need to strengthen research in Latin America to consolidate its own capabilities and respond to the ethical, technical, and social challenges posed by the adoption of AI in government management.
Copyright (c) 2025 Bryan Smith Contreras-Yupanqui (Author) https://creativecommons.org/licenses/by/4.0
Ethical AI for Personalized Banking: Addressing Bias and Fairness Challenges
Introduction: The integration of Artificial Intelligence (AI) into personalized banking has enhanced service delivery in areas such as loan processing, credit assessment, and fraud detection. Despite these advancements, ethical concerns, especially algorithmic bias and lack of fairness, pose significant challenges. This study addresses the need for equitable AI systems that promote transparency, fairness, and regulatory compliance in the banking sector. Objective: This study aims to develop and implement a comprehensive framework for integrating ethical principles into AI-driven banking systems, with a focus on mitigating algorithmic bias, enhancing fairness, and improving transparency in personalized banking services. Methods: A comprehensive methodology is proposed that integrates bias-aware data collection, fairness-constrained machine-learning models, and explainable AI (XAI) techniques. Tools such as Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) are applied to interpret model outputs. Adversarial debiasing and fairness-aware learning algorithms were employed to identify and mitigate systemic biases in financial data. Alternative data sources, including utility and rental payment histories, were incorporated to enhance inclusivity. Results: The implementation of the proposed framework demonstrates improved fairness in decision-making without significantly compromising model accuracy. Bias metrics show measurable reductions in disparate impacts across the demographic groups. Explainability tools enhance transparency, enabling a more transparent communication of AI decisions to both users and regulators. Conclusions: Embedding ethical principles into AI-driven banking systems is critical to ensuring fairness, regulatory alignment, and public trust. The structured framework presented in this study supports the development of responsible AI systems to mitigate bias, enhance explainability, and foster financial inclusion. This approach serves as the foundation for building equitable and accountable AI applications in modern banking.
Algorithmic transparency- Algorithmic bias
Copyright (c) 2025 Muthu Selvam (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the use of artificial intelligence in the management of logistics processes: Approaches and benefits
This study explores artificial intelligence (AI) applications to improve logistics within the Belt and Road Initiative’s Middle Corridor, focusing on Ukraine amid geopolitical disruptions. Its objective is to quantify AI's impact on delivery times, inventory costs, fuel consumption, and network resilience in this complex region. Employing a mixed-method approach, the research combines Social Network Analysis (SNA) to identify key nodes and bottlenecks with simulation modelling based on empirical data from 2019 to 2023, including GPS tracking and port metrics. Results show AI integration reduces delivery times by 11,7 %, cuts inventory holding costs by 16,3 %, and lowers fuel consumption by 9,2 %. SNA reveals enhanced connectivity and efficiency at critical hubs such as Kyiv and Odesa, strengthening Ukraine’s strategic logistics role. The study concludes that AI-driven optimizations significantly boost corridor efficiency, resilience, and sustainability. It recommends future work on real-time data integration and broader AI applications to support adaptive, greener supply chains across politically sensitive trade routes.
Copyright (c) 2025 Yuliia Rud , Nataliia Sapotnitska , Natalia Chaplynska , Mykola Palasevych, Oksana Kudriavtseva (Author) https://creativecommons.org/licenses/by/4.0
Generative artificial intelligence in tourism: current status and emerging trends
Introduction: 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.
Copyright (c) 2025 Rony Flores-Flores, Katty Alamo-Larrañaga, Elia Anacely Córdova-Calle, Jonathan Lee Arévalo-Pinchi, Jesús Rodríguez-Sánchez, Jorge Raúl Navarro-Cabrera (Author) https://creativecommons.org/licenses/by/4.0
Integrating AI-Based Natural Language Processing in Vocational Education: Usability, Learning Gains, and Student Engagement in Indonesia
Introduction: The advancement of Artificial Intelligence (AI) has brought substantial changes to education, particularly through AI-based digital assistants. Objective: This study developed and evaluated an AI-powered digital assistant equipped with Natural Language Processing (NLP) capabilities, specifically designed for Indonesian vocational schools. Methods: Adopting the 4D development model (Define, Design, Develop, Disseminate), the system was created using machine learning algorithms and NLP to enhance interactivity and personalization. The assistant enables natural language interaction, provides real-time feedback, and adapts learning material difficulty to students’ comprehension levels. The system was tested with 100 vocational school students, with usability assessed using the System Usability Scale (SUS) and learning gains measured through pre- and post-tests. Results: Results showed a SUS score of 71.05, indicating good usability, and a significant improvement in post-test scores compared to pre-test scores (p < 0.001), reflecting enhanced conceptual understanding, engagement, and motivation. Conclusions: These findings demonstrate the potential of AI-powered NLP assistants to enrich vocational education and prepare students for technology-driven industrial demands.
Copyright (c) 2025 Geovanne Farell , Delsina Faiza, Vera Irma Delianti , Rido Wahyudi, Agariadne Dwinggo Samala , Nurullah Taş (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Artificial Intelligence on the Development of Methods of Critical Text Analysis in Modern Philology
Introduction: this study aimed to evaluate the influence of artificial intelligence (AI), particularly deep learning and natural language processing (NLP) technologies, on the transformation of critical text analysis in contemporary philology. Aim: the research focused on how AI-driven approaches modify traditional linguistic and literary methodologies. Methods: a qualitative literature review was conducted to examine recent academic contributions at the intersection of philology and AI. Sources were selected from peer-reviewed journals covering linguistics, computational philology, and digital humanities. Results: the analysis revealed that AI-based algorithms, especially deep learning models, enhanced the detection of latent textual structures such as lexical patterns, stylistic markers, and semantic clusters. These technologies facilitated more accurate authorship attribution and allowed for the investigation of large corpora beyond the capacities of manual analysis. However, findings indicated that while AI could identify patterns and linguistic regularities, it lacked the ability to interpret deeper cultural, emotional, and symbolic meanings embedded in literary texts. Conclusions: the integration of AI into philological research offers valuable computational tools that expand analytical possibilities without displacing the interpretive role of the human scholar. AI technologies serve as a methodological extension, enhancing the precision and scope of critical analysis. Ultimately, the use of AI enriches the study of literature by uncovering patterns inaccessible to traditional methods, while preserving the necessity of human insight for contextual and interpretative depth.
Copyright (c) 2025 Yuliia Rud, Nataliia Sapotnitska , Natalia Chaplynska , Mykola Palasevych, Oksana Kudriavtseva (Author) https://creativecommons.org/licenses/by/4.0
AI Empowers Chinese Shaolin Kung Fu Movies: The Transformation Path from “Visual Spectacle” to “Cultural Creation
Introduction:In the dual context of globalization and digitalization, Shaolin kung fu films are facing unprecedented opportunities for transformation and challenges. As one of the most culturally distinctive film genres representing China's excellent traditional culture, Shaolin kung fu films have embodied the essence of Eastern philosophy—the unity of Zen and martial arts—since the release of *Shaolin Temple* (1982). However, existing research has largely remained at the level of typological analysis or qualitative interpretation of cultural symbols, failing to adequately address the paradigm shift in film narrative in the digital age. With breakthrough developments in generative AI, computer vision, and other technologies, film studies urgently need to establish new theoretical frameworks to explain how technology is reshaping cultural expression. This study adopts an interdisciplinary perspective at the intersection of film semiotics and technology philosophy. As an important carrier of national culture, Chinese Shaolin kung fu films urgently need to break free from the traditional narrative constraints of “visual spectacles” and achieve a paradigm shift from technology-driven to culture-driven innovation. Methods:1) Text and symbol mining: Conduct semantic network analysis on the scripts and dialogues of classic films such as Shaolin Temple;2) Multimodal collaborative analysis: Reconstruct narrative units using generative AI (such as the GPT-4 multimodal model);3) Cross-cultural comparative research: Interpret differences in symbolic meanings and propose AI adaptation and cultural generation strategies. Results:1) The “Zen and martial arts unity” symbol exhibits centrality in the semantic network, with a significant association between slow motion and Zen-like imagery; 2) In multimodal analysis, the overlapping effects of action and soundtrack significantly enhance emotional resonance; 3) Semantic network analysis and symbolic metaphor interpretation of the film script, dialogue, and action design are conducted, with generative AI used to reconstruct narrative units. Conclusions:1) The study validated the feasibility of quantitative analysis of cultural symbols and AI adaptation strategies; 2) A localized technology ecosystem was constructed to balance technology dominance and cultural authenticity; 3) In the future, further attention should be paid to balancing technological ethics and cultural sovereignty to ensure that AI truly serves the in-depth dissemination of cultural values.
Copyright (c) 2025 SUN Yongtai, Sastra Laoakka (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence in Latin American higher education: implementations, ethical challenges, and pedagogical effectiveness
Artificial intelligence is establishing itself as a catalyst for transformation in the regional university sector, generating growing yet uneven academic output. This research conducted a systematic review following the PRISMA methodology on applications of artificial intelligence in Latin American higher education. The results from the 421 studies obtained during the bibliometric stage indicate that research is geographically and institutionally concentrated in a limited set of approaches and practices. In this regard, a notable prevalence of studies on Machine Learning applications, as well as Natural Language Processing, was observed. From a practical standpoint, 30 studies were selected for qualitative analysis. These texts agreed that the implementation process of these technologies continues to face structural challenges. Notably, poor infrastructure conditions, as well as deficiencies in teacher training, were identified as the main obstacles to implementing these technologies. The analyzed studies also concurred on the inadequate treatment of algorithmic biases or data protection in application policies proposed by the literature. Consequently, a key recommendation of this research is the urgent need for studies aimed at evaluating short-term outcomes, as well as analyzing the long-term sustainability of such innovations.
Copyright (c) 2025 Víctor Hugo González Torres, Elisabeth Viviana Lucero Baldevenites, Manuel de Jesús Azpilcueta Ruiz Esparza, Pedro Luis Bracho-Fuenmayor, Claudia Patricia Caballero de Lamarque (Author) https://creativecommons.org/licenses/by/4.0
From Awareness to Practice: Exploring the Knowledge, Attitudes, and Practices of Secondary ESL Teachers in the Philippines Toward ChatGPT in Education
The 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.
Copyright (c) 2025 Julie Uy Cabato (Author) https://creativecommons.org/licenses/by/4.0
Integration of Artificial Intelligence into the Curricula of Higher Education Institutions
Introduction: The study explored how artificial intelligence (AI) is transforming the teaching of scientific disciplines by enabling personalized learning and simplifying the understanding of complex concepts. Particular attention was given to the role of AI tools in providing individual academic support. Methods: A qualitative analysis was conducted based on 71 publications retrieved from Google Scholar, ResearchGate, and Scopus databases. The selected sources covered theoretical and empirical research on AI implementation in higher education curricula. Results: The findings indicated that AI technologies enhanced students’ engagement with the learning material and facilitated comprehension of abstract and complex phenomena across various disciplines. However, several barriers to integration were identified. These included insufficient technical infrastructure and inadequate teacher training, both of which limited the effective use of AI tools in many higher education institutions. Conclusions: To ensure successful AI integration into educational programmes, it is essential to establish robust technological infrastructures and develop comprehensive professional development initiatives for academic staff. When effectively implemented, AI has the potential to support individualized learning experiences and significantly influence the broader educational ecosystem by fostering the evolution of student-centered thinking.
Copyright (c) 2025 Iryna Zinkiv, Iryna Konovalova, Iryna Polska, Olena Roshchenko, Tetiana Rozhnova (Author) https://creativecommons.org/licenses/by/4.0
Trends in The Use of Artificial Intelligence for Automating Assessment in English Language Teaching
This study aims to analyze the potential of using artificial intelligence (AI) for automating assessment and English language teaching, as well as its impact on personalizing the learning process. A systematic literature review was conducted using a targeted search strategy across relevant academic databases, including Google Scholar, ResearchGate, and Scopus. As a result of this review, 52 academic papers dedicated to the use of AI for automating assessment and foreign language teaching processes were selected. The findings revealed that AI demonstrates significant potential in personalized learning, skill development, assessment automation, and enhancing teachers' professional development. The possibility of using AI to automate the knowledge assessment process, thereby reducing the workload for educators and increasing the objectivity of evaluation, was explored. The article explores the potential applications of technologies like ChatGPT4 and virtual reality in automating educational processes and assessments, emphasizing their capacity to enhance access to education without limitations of time and space. However, the paper acknowledges the challenges of integrating AI systems into education, encompassing ethical, pedagogical, and technical considerations. Ultimately, the article examines strategies to optimize the benefits of AI in higher education and anticipates the future trajectory of AI development within the educational context.
Copyright (c) 2025 Anna Khomyk, Tetyana Dniprovska, Olga Kondrashova, Antonina Varlakova, Oksana Ivasiuk (Author) https://creativecommons.org/licenses/by/4.0
Teaching German within digital paradigm of education: AI-based approaches and tools
In view of the increased popularity of AI tools in teaching foreign languages, particularly German, and the corresponding concerns that arose, this article explored the futuristic prospects of learning German with AI. It examined how these technologies had revolutionized the learning process and what learners could expect in the future. The study’s methodology was based on a systemic paradigm and involved the use of content analysis and elements of case studies, relying on a wide array of literature sources extracted from general and specialized scientometric databases. The findings showed that AI in language teaching represented a powerful approach to engaging students and enhancing learning outcomes. The most innovative methods, such as the integration of massively multiplayer online role-playing games (MMORPGs) into educational processes, yielded the most effective results. The study attempted to outline the correlation between various AI-based teaching approaches and existing educational theories that characterized the contemporary educational landscape. Furthermore, it proposed an appropriate schematic model that could serve as a foundation for further research in the field, including studies with an interdisciplinary focus.
Copyright (c) 2025 Nataliia Shevtsova, Alina Аndroshchuk, Viktoriya Syno, Iryna Aleksandruk, Oleksandr Maliuha (Author) https://creativecommons.org/licenses/by/4.0
Adoption of artificial intelligence technologies in Argentine external auditing
This study analyzes the adoption of artificial intelligence (AI) technologies in external auditing in Argentina, within a context where these tools promise to optimize processes and enhance the quality of professional judgment. Despite a high level of awareness regarding AI, its practical application remains limited and uneven. The objective was to analyze the adoption of artificial intelligence technologies in external auditing in Argentina. A mixed-methods approach with a descriptive design was employed. A total of 236 certified public accountants were surveyed between August 2024 and February 2025, and the quantitative findings were complemented by semi-structured interviews. The results show that although 97% of respondents are familiar with the concept of AI, only 12% apply it in their auditing work. The main barriers identified were the lack of specialized training, limited technical skills, and organizational resistance to change. Among the most valued benefits are time savings, increased accuracy, and improved detection of irregularities. The analysis allowed for the identification of three user profiles: young innovators, neutral professionals, and older individuals willing to adopt but lacking training. The study concludes that promoting targeted training programs, clear regulatory frameworks, and an innovation-oriented organizational culture is essential to bridge the gap between technological discourse and its effective implementation.
Copyright (c) 2025 Juan Ignacio Ruiz, Verónica Olocco, Alfredo Mario Baronio (Author) https://creativecommons.org/licenses/by/4.0
Research on Intelligent Recommendation Algorithm of Short Videos Based on Graph Neural Network
The rapid development of short video platforms has put forward higher requirements for the accuracy and personalization of content recommendation systems. In this paper, a short video recommendation algorithm based on Graph Neural Network (GNN) is studied, which improves the recommendation performance by fusing multimodal features such as video, audio, and text. The key technologies such as graph convolution neural network, graph attention network and graph pooling operator are analyzed, and a multimodal recommendation framework is constructed by combining self-supervised contrastive learning and local feature encoder to effectively deal with complex user-content interactions. In this paper, several algorithms are compared on TikTok and MovieLens datasets. The experimental results show that the SHL algorithm significantly improves the recommendation accuracy and user personalized satisfaction on TikTok and MovieLens datasets, which is generalizable.
Copyright (c) 2025 Qiuye Guo, Sanghyun Kim (Author) https://creativecommons.org/licenses/by/4.0
AI evolution and its role in transforming the automation of commercial activities
This article examined the impact of artificial intelligence (AI) on the automation of business processes, focusing on how intelligent systems enhanced management efficiency and operational optimization. Special attention was given to cognitive neuro-fuzzy models and their role in transforming business processes in the digital era. The study was timely, considering the exponential growth of data and the complexity of modern organizational structures, which demanded fast, accurate, and adaptive management solutions. AI technologies provided such capabilities, while companies that failed to adopt them risked losing competitive advantage amid ongoing digital transformation. The study aimed to develop and justify a conceptual approach to automating business processes through AI. To achieve this, two primary methods were applied: cognitive modeling using semantic M-networks to reflect human imaginative thinking in process structures, and reinforcement learning to optimize processes based on feedback mechanisms. The methodology combined theoretical literature analysis, mathematical modeling, and empirical examination of real business processes. The findings demonstrated that integrating AI significantly improved overall business process efficiency by reducing complexity, costs, and feedback loops, while enhancing control, regulation, and financial outcomes. The M-network model illustrated how AI adapted processes to dynamic environments and supported decision-making through visualized cognitive maps. Future research directions included advancing cognitive learning algorithms to handle larger datasets, designing adaptive AI interfaces tailored to individual user behavior, and exploring AI’s influence on cross-functional collaboration to foster comprehensive digital management ecosystems.
Copyright (c) 2025 Mykhailo Lukash, Yevhenii Chuprun, Oksana Lysak, Anatolii Husakovskyi, Kyrylo Hanhanov (Author) https://creativecommons.org/licenses/by/4.0
Global Digital Transformation: Ensuring the Protection of IP Rights
Today, 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' 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.
Copyright (c) 2025 Iurii Bedratyi, Nadiia Vasylieva, Olena Perunova, Oleksii Volokhov, Larysa Chekmarova (Author) https://creativecommons.org/licenses/by/4.0
Attitude, Acceptability, and Perceived Effectiveness of Artificial Intelligence in Education: A Quantitative Cross-sectional Study among Future Teachers
This 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.
Copyright (c) 2025 Jay Rodel C. Serdenia, Alexandhrea Hiedie Dumagay, Keir A. Balasa, Elenieta A. Capacio, Lovelle Diocess S. Lauzon (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Gamification to Sustain Student Motivation and Emotional Resilience in Higher Education During Wartime: Case Studies from Ukraine
In the conditions of war in Ukraine, the educational process underwent profound transformations, accompanied by a decrease in student motivation and an increase in emotional exhaustion. The relevance of the topic was due to the need to find effective psychological and pedagogical tools to support student engagement in times of crisis. The purpose of the study was to find out the impact of gamification on student motivation; the object was the educational process in higher education institutions during the war. The research methodology was based on a questionnaire, comparative analysis, qualitative interviews, and empirical observation of gamified educational practices in three higher education institutions. The results of the study showed that team gamification, the use of adaptive online platforms, and instant feedback mechanisms were the most effective in wartime. Gamification was shown to increase academic engagement, reduce anxiety, and raise satisfaction with the learning process. Particularly high rates were recorded among psychology and engineering students. Gamified elements, such as virtual rewards, interactive missions, and cooperative tasks, proved to be effective not only in terms of learning but also in providing emotional support. The practical significance of the results lay in the possibility of adapting the cases to other educational contexts and developing strategies to overcome motivational decline during a crisis. The findings could be useful for teachers, educational administrators, and content developers who were looking for innovative solutions in the face of uncertainty.
Copyright (c) 2025 Marianna Zhumbei, Halyna Apelt, Nadiia Savchuk, Oleksandr Akimov, Inna Tsymbal (Author) https://creativecommons.org/licenses/by/4.0
Tinkercad and mBlock: Tools for Educational Robotics and Coding Learning A Case of Ghandi Primary school Morocco
This study explores the impact of Tinkercad and mBlock on the en gagement and computational thinking skills of students at Ghandi Primary School. Through interactive robotics and coding activities, students demonstrated increased motivation, problem-solving abilities, and confidence in programming. Using Arduino and block-based coding, they developed a deeper understanding of STEM concepts in a hands-on learning environment. The findings suggest that integrating educational robotics into primary education enhances student engage ment and fosters essential 21st-century skills.
Copyright (c) 2025 Fatimazahra Ouahouda, Khadija Achtaich, Naceur Achtaich (Author) https://creativecommons.org/licenses/by/4.0
A Machine Learning Model for Diagnosis and Differentiation of Schizophrenia, Bipolar Disorder and Borderline Personality Disorder
Schizophrenia, bipolar disorder, and borderline personality disorder present overlapping symptoms, complicating accurate diagnosis. Misdiagnosis leads to inappropriate treatment, increased patient distress, and higher healthcare burdens. This study develops a machine learning model integrating clinical, neuroimaging, and behavioral data to improve diagnostic accuracy. The model utilizes Convolutional Neural Networks (CNNs) for neuroimaging, Gradient Boosting Machines (GBMs) for structured clinical and behavioral data, and Recurrent Neural Networks (RNNs) for speech analysis. The combined model demonstrated superior accuracy (94.1%) compared to individual models. SHAP analysis identified key diagnostic features, including specific brain regions, cognitive measures, and speech patterns. External validation confirmed robustness, highlighting the model’s potential as a clinical decision-support tool. Future research should focus on enhancing model interpretability and real-time diagnostic support.
Copyright (c) 2025 Salma Abdel Wahed, Rama Shdefat , Mutaz Abdel Wahed (Author) https://creativecommons.org/licenses/by/4.0
Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview
Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities.
Copyright (c) 2024 Amit Kumar Dinkar, Md Alimul Haque, Ajay Kumar Choudhary (Author) https://creativecommons.org/licenses/by/4.0
IA´ Tools for the development of investigative skills
This article explores how the artificial intelligence (IA) it is transforming the education in natural sciences by means of strategies pedagogic innovators. The IA allows the learning personalization, adjusting the content and the rhythm to the individual necessities of the students, what improves the understanding and retention of complex concepts significantly. Also, the use of simulations and virtual models believe interactive and visual learning environments, enriching the educational experience. These tools also foment the development of critical and creative skills, promoting a more active and collaborative approach in the resolution of scientific problems. On the whole, these strategies not only improve the effectiveness of learning, but rather they also prepare the students to face the challenges of the XXI century with a solid base in science and technology.
Copyright (c) 2023 Mayra Alejandra Gaviria Alvarado (Author) https://creativecommons.org/licenses/by/4.0