Conference Agenda

This study examines the effectiveness of audiovisual media, including educational videos, songs, and interactive games, in enhancing English vocabulary acquisition in 4-year-old children. A qualitative, non-experimental, descriptive research approach was employed, using observation and audiovisual recordings to analyse children's interactions and responses. The findings indicate that audiovisual resources significantly improve vocabulary retention, word-image association, and listening comprehension. However, limitations were identified in material design and interactivity, suggesting the need for further optimization. These results support the integration of audiovisual media with pedagogical strategies to enhance early language learning in preschool education.

This systematic review analyzes the impact, trends, and challenges of AI adoption in higher education, following the PRISMA framework with a mixed-methods approach. A comprehensive search of Scopus and Web of Science (2019-2024) identified key AI applications, including personalized learning, automation, and learning analytics. The results show a growing interest in AI-driven education, with intelligent tutoring systems, chatbots, and adaptive learning platforms improving teaching efficiency and student engagement. Regional differences highlight Asia's focus on data analytics and Latin America's emphasis on digital literacy. Despite the benefits of accessibility, personalization, and administrative efficiency, challenges remain, including algorithmic bias, transparency, and ethical concerns. The study concludes that AI is reshaping education but requires responsible implementation to maximize its benefits while ensuring equity and ethical integrity.

The purpose of the study was to determine the relationship between digital competence and teaching practice in virtual classes for adults in a private university during 2024, aligning with SDG 4 (Quality Education) of the 2030 Agenda. The research was of a basic descriptive type, with mixed approach and sequential correlational-explanatory design. The population consisted of 485 teachers from a private university, selecting a probabilistic sample of 216 teachers. Validated questionnaires were used: “DigCompEdu Check-In” for digital competence, with 22 items and six dimensions, and another with 28 items for teaching practice. In addition, focus groups, observation and documentary analysis were used. Spearman's correlation analysis showed a moderate positive correlation (0.585, p<0.01) between both variables, showing that the greater the digital competence, the better the teaching performance. It was concluded that strengthening digital competence in teachers contributes to higher quality pedagogical practice, identifying opportunities in the use of interactive tools and collaborative methodologies.

The objective of this article is to conduct a scoping review on the use of artificial intelligence (AI)-based technologies in English as a Foreign Language (EFL) teaching. The methodology employed follows PRISMA ScR guidelines, focusing on synthesizing existing evidence and evaluating the breadth and diversity of the literature on this topic. The main findings reveal that the implementation of various AI technologies, such as neural networks, deep learning, natural language processing, intelligent teaching models, optimization algorithms, and robotic and interactive systems, has significantly improved the personalization of language learning, educational assessment, and student engagement. Neural networks and deep learning have proven particularly effective in optimizing educational processes and enhancing interaction and efficiency in learning. Therefore, AI-based technologies have great potential to enhance learning effectiveness, but they also require well-defined strategies for their responsible and equitable implementation in the educational field.

This academic work aligns with Sustainable Development Goal 4, "Quality Education," and aims to determine the correlation between the use of the Generative Language Model and critical thinking in students at a Peruvian state higher education institution. The study adopts a quantitative approach with a correlational design, using a sample of 155 students selected through probabilistic sampling. The results show that the majority (56%) of participants display a medium level both in the use of the Generative Language Model and in critical thinking skills. A positive and significant correlation (Spearman's Rho = 0.999) was observed between the use of the Generative Language Model and critical thinking, enhancing skills in analysis, evaluation, inferential reasoning, problem-solving, and self-regulation. The main conclusion highlights the potential of the Generative Language Model as an effective educational tool to strengthen critical thinking, provided its use is accompanied by appropriate pedagogical guidance

This study investigates the impact of cloud computing on active learning methods in a higher education network security course. We analyze student performance over four years, comparing two control years (2021-2022) with two experimental years (2023-2024) where cloud computing was introduced. The results show a delayed, but ultimately positive, impact of cloud computing on learning outcomes, particularly in experiential tasks and problem-based learning. Additionally, we find an increase in female student participation in the second year of cloud computing use. The study also highlights the significant cost associated with providing individual cloud resources to each student. Finally, we observe asymmetry in cloud computing usage time, suggesting diverse learning patterns and technological engagement among students.