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The Impact of Artificial Intelligence on the Development of Methods of Critical Text Analysis in Modern Philology
Get PDFIntroduction: 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
Artificial Intelligence as a Pedagogical Resource in Initial Teacher Training
Get PDFThis 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
Integrating Artificial Intelligence for Science Teaching in High School
Get PDFThis paper studies the potential benefits and challenges of incorporating AI into science education for secondary-level schools. It explores how AI-driven tools can enhance personalized learning, improve student engagement, and reshape teaching methodologies while addressing concerns regarding equity, accessibility, and teacher-student interactions. A literature review and analysis of AI applications in education focused on adaptive learning technologies, interactive simulations, and AI-driven feedback systems. AI technologies, including ChatGPT, facilitate personalized learning through adaptive feedback that targets individual knowledge gaps and learning preferences, promoting a more profound comprehension of intricate subjects such as physics. Findings indicate that AI enhances learning experiences by providing personalized feedback, fostering interactive and collaborative learning environments, and supporting differentiated instruction. However, challenges such as limited access to technology, teacher training, and ethical considerations regarding data privacy must be addressed to ensure equitable AI implementation in education. AI has the potential to revolutionize science education by making learning more engaging and tailored to student needs. However, successful integration requires addressing challenges related to infrastructure, teacher training, and ethical concerns. This study highlights the need for comprehensive policies and professional development programs to maximize the benefits of AI while ensuring fair and effective implementation in science education
Prognosis of artificial intelligence in education
Get PDFThe Higher Education Institutions require emphasis on disruptive intelligent systems which includes Artificial Intelligence that challenges conventional methods with improved products and services. This study aimed to know the trend artificial intelligence in engineering education. Specifically, it aimed to know the profile of the respondents, know the level of utilization of artificial intelligence tools in engineering education, know if there is significant relationship between profile of respondents to the AI tools used in engineering education, and propose a model of artificial intelligence in engineering education. This paper used quantitative correlational methods of research. Result showed that majority of the respondents has more work experience, found that most teachers have five years or more of experience and found that in terms of educational attainment, majority of the respondents had master’s degree. Artificial intelligence tools are generally “Sometimes Utilized” in engineering education and the respondents\u27 profiles had no significant relationship on the use of the AI technologies, which are often occasionally used in engineering education. To fully utilize AI capabilities in engineering education, the model achieved offers a number of particular actions, including institutional in-house training, awareness campaigns, research conferences, and informal information exchange
Integrating AI in Language Learning: Boosting Pragmatic Competence for Young English Learners
Get PDFThis article explores the role of artificial intelligence (AI) tools in enhancing pragmatic language skills of young English learners (ELs). It defines terms such as interlanguage pragmatics, pragmatic competence, and intercultural communicative competence, and discusses key concepts in pragmatics, including maxims of discourse, implicatures, presuppositions, and speech acts. The article emphasizes the importance of sociocultural context and interaction in promoting ELs’ pragmatic skills in the second language (L2). It also explores different ways AI can be utilized to teach essential pragmatic skills, including understanding implicatures, making inferences, interpreting presuppositions, applying speech acts properly, and adhering to the maxims of discourse for effective communication in the target language – specifically, English. By creating immersive and interactive learning environments, AI chatbots, dialogue systems, and platforms facilitate contextual learning that engages ELs and promotes practical language use. The article concludes by discussing the limitations and challenges related to teaching pragmatics to language learners, advocating for targeted research efforts to enhance our understanding of pragmatic development among young ELs and the role of AI tools in this proces
Research on the path to improve the teaching ability of college teachers based on artificial intelligence
Get PDFWith the rapid development of artificial intelligence technology, its application in the field of education has provided a new path for improving the teaching ability of college teachers. This paper explores the role of artificial intelligence in improving the teaching ability of college teachers in terms of teaching design, classroom management, and student evaluation through literature analysis, case studies, questionnaires, and interviews. The study found that artificial intelligence technology can significantly optimize teachers\u27 teaching design ability, classroom management ability, and student evaluation ability, but it also faces challenges such as data security and technology dependence. This paper proposes suggestions for optimizing the path of artificial intelligence to improve the teaching ability of college teachers, in order to provide a reference for the reform of higher education
Artificial Intelligence as the Call of the Times: Attitudes of Higher Education Teachers across Gender, Generational Cohorts, and Length of Service in the Context of Education 5,0
Get PDFArtificial Intelligence had reshaped education worldwide and redefined how teachers taught and how students learned. Yet in the Philippine higher education sector, particularly in Southwestern Mindanao, progress remained uneven because of gaps in infrastructure and training. The study aimed to determine the attitudes of 648 in-service educators from state universities and colleges toward AI in education, with attention to gender, generational cohort, and length of service as influencing factors. The study used a quantitative cross-sectional design and analyzed the data through descriptive statistics, independent samples t-tests, and one-way ANOVA. Results showed that teachers held generally positive attitudes toward AI (mean = 4,19, SD = 0,94), while negative attitudes were relatively low (mean = 2,27, SD = 0,82). Significant differences appeared across gender (t (646) = 7,03, p < 0,001), generation (F (3,644) = 2391,43, p < 0,001), and length of service (F = 8,45, p < 0,001). Female and younger educators, particularly those from Generation Z and Millennials, showed stronger positive attitudes, whereas teachers with longer service were more cautious. The findings revealed that openness to AI was shaped by demographic and professional factors. These findings suggest that AI adoption in higher education is shaped not only by technology itself but also by teachers’ demographic backgrounds and professional contexts. The study recommends targeted professional development and inclusive policies to strengthen AI literacy, address concerns, and align AI integration with the human-centered vision of Education 5,0
Implementation of Artificial Intelligence Systems in Public Administration in Latin America: Impacts and Challenges
Get PDFIntroduction: 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
Integration of Artificial Intelligence into the Curricula of Higher Education Institutions
Get PDFIntroduction: 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
Integrating AI to Assess Community Roles in Environmental Safeguarding During Mining: Implications for ESIA in SSA
Get PDFThis study investigates the role of local communities in environmental safeguarding during mining operations in Sub-Saharan Africa (SSA) and its implications for Environmental and Social Impact Assessments (ESIAs). While mining drives economic development, it often imposes environmental and social costs on local populations. The study critiques existing ESIA frameworks for privileging top-down, technocratic models that marginalize community voices. Using a systematic scoping review of 62 peer-reviewed empirical studies published since 2010, the research analyzes community participation and safeguarding practices through thematic coding and AI-powered tools like natural language processing. The findings underscore that local communities possess unique monitoring capacities, contextual knowledge, and culturally grounded environmental ethics that can enhance ESIA efficacy. These communities often respond more effectively than regulatory authorities to environmental infractions. The study also identifies structural barriers such as tokenistic participation, poverty, and policy exclusion that undermine meaningful engagement. It recommends embedding community-driven perspectives within ESIA processes by strengthening collaborative frameworks, recognizing indigenous knowledge systems, and leveraging AI to ensure inclusive and transparent evaluations. Furthermore, it argues for a shift toward participatory governance models that empower communities as co-regulators of environmental standards. By reframing ESIA as a dynamic socio-environmental negotiation, the study offers practical insights for policy reform, corporate responsibility, and sustainable development in SSA’s mining sectors