Conference Agenda

The expansion of virtual laboratories in engineering education has sparked debate about the extent to which these environments truly contribute to achieving learning outcomes in introductory courses, traditionally based on face-to-face practical experiences. This study analyzes the impact of virtual laboratories in an introductory engineering course using a quasi-experimental pretest-posttest design with a non-equivalent comparison group. Two cohorts were compared: one group that worked with traditional laboratories (n = 96) and another that completed all practical experiences using virtual laboratories integrated into the institutional LMS (n = 100), maintaining identical content, learning outcomes, and general assessment framework. Conceptual achievement (pre/post tests), procedural and experimental skills (rubrics applied to laboratory reports), attitudes toward the laboratory experience (Likert scale), and participation in practical activities were assessed. The results show that the group using the virtual laboratory achieved significantly greater normalized conceptual gain and higher scores across all assessed procedural dimensions, with moderate-to-large effect sizes, particularly in data recording and analysis and results interpretation. Furthermore, students in the virtual group reported a greater perceived usefulness of the laboratory for understanding the theory, greater interest in experimentation, and greater confidence in applying concepts in practical contexts. These findings indicate that, when explicitly designed and aligned with course outcomes, virtual laboratories are not only a viable alternative to traditional laboratories in introductory engineering courses, but can also enhance conceptual, experimental, and attitudinal learning, providing a solid framework for hybrid models and future curricular innovations.

The general objective of this research was to determine if the implementation of the Building Blocks workshop is effective in improving the creativity of preschoolers in the El Porvenir district. This research is a quantitative approach, applied, non-experimental design and descriptive in scope. The population studied in its entirety was 29 preschoolers from 3 to 5 years old. The technique used was observation and the observation sheet was used as an instrument. For the analysis of results, descriptive and inferential statistics were used, the data of which were entered into the SPSS V29 system. Significance results were obtained from the Shapiro Wilk test (Sig. <0.003) taken in the post-test, in addition, it was possible to show that there is a high degree of difference in the results obtained, before and after the Implementation of the Workshop, this is obtained with the Wincoxon comparison test (Z= -4.296 Sig. <.000). It was concluded that the implementation of the “Building Blocks” workshop is significantly effective in improving creativity and is an ideal resource in preschools in the El Porvenir district.

This research addresses the use of virtual simulators and augmented reality (AR) in university academic innovation, considering their impact on learning and pedagogical transformation in universities. A systematic literature review was carried out between 2023 and 2025, with the aim of identifying the benefits and challenges of these technologies in higher education. The study used a quantitative approach and a non-experimental design, with thematic content analysis of previous studies on the use of simulators and AR in educational contexts. The results revealed that emerging technologies, such as virtual simulators and AR, improve the understanding of abstract concepts, foster student motivation, and facilitate active participation in learning, especially in STEM disciplines. However, significant barriers are also identified, such as the lack of adequate technological infrastructure and insufficient teacher training, which limits their effective implementation in many universities, particularly in resource-limited regions. These findings highlight the need for investment in educational infrastructure and teacher training to ensure the successful integration of these tools into educational programs. The review also suggests that, despite the challenges, the use of simulators and AR represents a key opportunity to improve educational quality and prepare students for a digitalized environment.

This study offers an interpretation of a scientific and methodological model for the development of educators’ functional literacy under conditions of integrating artificial intelligence technologies into educational practice. The relevance of the research is обусловлена because contemporary AI systems extend beyond the framework of instrumental digitalization and, consequently, transform the structure of professional pedagogical thinking by redistributing cognitive and analytical functions between the educator and algorithmic systems. Under these conditions, educators’ functional literacy can no longer be understood merely as an extension of digital competence and therefore requires conceptual reconsideration. In particular, recent international reviews emphasize that artificial intelligence is transforming the traditional “educator–student” relationship into a new triadic configuration of “educator–AI–student,” thereby necessitating a fundamental rethinking of the educator’s professional roles.

We propose a matrix-based model that integrates three levels of functional literacy development (adaptive, integrative-operational, and reflective-projective) with four functional blocks of pedagogical activity. The study demonstrated that the development of functional literacy is associated not with the intensity of AI usage but with the qualitative deepening of pedagogical judgment, reflection, and professional responsibility. The practical significance of the proposed model lies in its applicability to the design of professional development programs and to the evaluation of educational practices involving the use of artificial intelligence.

Selecting the correct statistical test is a frequent challenge for students and researchers with limited mathematical backgrounds. Incorrect choices can undermine analytical rigor and threaten research validity. This study presents the design and expert validation of the Velarde-Camaqui Diagram, a visual decision-support tool that guides users in selecting inferential statistical tests. A mixed-methods validation was conducted with 14 PhD experts in education and statistics from several countries. Experts assessed clarity, coherence, relevance, and usability using a 12-item Likert-scale questionnaire (1–3) and provided qualitative feedback. Quantitative analysis revealed high acceptance rates (93–100%), confirming strong perceived relevance, while coherence for correlations and associations scored lowest (85%), indicating room for structural refinement. Qualitative feedback suggested improvements in color coding, visual hierarchy, and instructional clarity. Overall, the results provide initial evidence supporting the diagram’s content validity and perceived usability as a structured aid for statistical decision-making in higher education.

Student management often faces challenges related to organizing academic information and producing timely, accurate reports. To address these limitations, this work proposes an approach for improving student management using Gemini 2.5 Pro. The development process involved four stages: (i) preprocessing an academic dataset containing 311 records and 35 variables; (ii) designing tailored prompts to generate personalized reports; (iii) integrating the model through the Google Generative AI API; and (iv) implementing a web-based prototype capable of producing performance summaries, study plans, and formal communications. Evaluation with a student user showed that the system successfully generated study plans, overall performance summaries, recommendations, review plans, progress analyses, attendance reports, and insights on strengths and learning habits. The outputs were coherent, contextually relevant, and clearly articulated. These results highlight the potential of advanced AI tools to support school management processes, streamline academic tasks through automation, and enhance pedagogical monitoring by leveraging real institutional data.