Conference Agenda

Artificial intelligence applications are fundamental tools for the development of communication. However, they are not planned or implemented in the educational process, despite the fact that Sustainable Development Goal (SDG) 4 calls for their use to reduce inequality and develop equitable education. The research started from the general objective: to identify the artificial intelligence applications used by teachers in the development of oral and written communication in secondary school students in the Cajamarca Region, 2025. It followed a descriptive observational approach, under the parameters of a qualitative, flexible, and interactive case design, seeking to analyze the planning and implementation of artificial intelligence in communication development. The sample consisted of twenty-two teachers in the area of communication, who facilitated the annual program, units, and learning sessions, analyzed using a documentary analysis guide. The results reveal that the artificial intelligence applications used by teachers in the development of oral and written communication are ChatGPT, Copilot, text generators, grammar correction assistants, voice synthesizers, and automatic feedback tools, which improved clarity, cohesion, and coherence in oral and written production.

Keywords. Artificial Intelligence, Planning, Application, Inclusion, and Communication.

The rapid development of generative artificial intelligence has radically transformed the ways in which visual images are created, interpreted, and circulated, giving rise to a new cognitive environment in which artistic production unfolds through the interaction of human intention and the latent probabilistic structures of the model. Traditional frameworks of artistic and visual literacy prove insufficient for describing these processes, as they presuppose fixed relationships between the subject, the image, and the cultural code.

In this study, we propose a conceptual model of digital-art literacy, interpreting artistic literacy in the age of AI as a hybrid cognitive competence that integrates four levels: semiotic–compositional, prompt-rhetorical, stylistic–generative, and algorithmic–semiotic.

We demonstrate that generative models transform not only the techniques of image production but also the epistemology of visuality, turning the image into a computational object that emerges within latent spaces.

We also discuss practical implications for engineering education, creative industries, professional communication, and critical media literacy.

On the basis of our analysis, we conclude that digital-art literacy constitutes a new form of cognitive culture essential for meaningful participation in the algorithmically augmented visual environment of the twenty-first century.

This study examines the integration of artificial intelligence (AI) into business management education and its influence on the development of organizational capabilities and customer-centric skills among university students in Lima, Peru. A quantitative explanatory approach was employed using a non-experimental, cross-sectional design and covariance-based structural equation modeling (CB-SEM) to evaluate the proposed conceptual model. The sample consisted of 385 undergraduate students enrolled in business management–related academic programs. The findings indicate statistically significant relationships between AI training and organizational capability development (β = 0.61; p < 0.001), as well as between organizational capabilities and customer-centric skills (β = 0.55; p < 0.01). Additionally, a direct effect of AI training on customer-oriented competencies was identified (β = 0.48; p < 0.05), highlighting the educational value of intelligent technologies in business contexts. Despite these positive outcomes, barriers such as limited practical exposure to AI tools, technological infrastructure constraints, and insufficient curricular integration were identified. The study proposes academic and institutional strategies to strengthen AI integration within business management curricula. Overall, AI emerges as a strategic component for developing organizational understanding and customer-focused competencies aligned with digital business transformation.

The rapid integration of Generative Artifi cial Intelligence (GAI) tools, particularly ChatGPT, into higher education has raised concerns about potential cognitive dependency and its impact on students' problem-solving skills. This study aims to classify student profi les based on their dependency on generative AI and their ability to solve complex problems using machine learning clustering techniques. A cross-sectional study was conducted with 847 engineering students from three Latin American universities. The AI Dependency Scale for University Students (EDIAU-15), the Complex Problem-Solving Inventory (IRPC-25), and the ChatGPT Frequency of Use Questionnaire (CFUC) were administered. K-means clustering with Davies-Bouldin optimization identifi ed four distinct student profi les: (1) Critical Autonomists (23.4%, n=198): low AI dependence, high problem-solving ability; (2) Balanced Integrators (31.2%, n=264): moderate AI use, preserved cognitive skills; (3) Dependent Compensators (28.6%, n=242): high dependence on AI, declining problem-solving ability; and (4) Passive Delegators (16.8%, n=143): severe dependence on AI, signifi cantly impaired cognitive functioning. ANOVA revealed signifi cant diff erences between profi les in academic performance (F=47.82, p<.001), metacognitive awareness (F=38.94, p<.001), and self-effi cacy (F=52.17, p<.001). Structural equation modeling confi rmed that AI dependence mediates the relationship between ChatGPT usage frequency and problem-solving ability (β=-0.43, p<.001).

Incorporating digital patternmaking tools in higher education can strengthen applied learning and academic commitment when embedded in face-to-face activities that combine guided practice, problem solving, and formative feedback. This study evaluated the perceived impact of a digital patternmaking tool implemented in on-campus courses of the BA in Fashion and Textile Design during the second semester of 2025. A descriptive post-intervention design was applied to 12 students (mean age 20.83 years, SD = 0.83); 91.7% were women. Deep-learning indicators reflected high interest and dedication: 72.7% reported that new topics were interesting and they devoted extra time, 81.8% worked with material that sparked their interest, and 63.6% used additional resources to understand relevant content; however, items linked to deeper exploration and active preparation were lower (45.5%, 36.4%, and 27.3%). Academic engagement was high, with one item reaching 100% favorable responses and energy/absorption items at 90% each. Perceived learning was globally positive, with 100% favorability for practical application of course content and for identifying key concepts; satisfaction and perceived learning quality were lower but still positive (66.7%). Usability results were also favorable: 100% intended frequent use, perceived ease of use reached 77.8%, and functional integration 88.9%. Qualitative responses highlighted technical barriers (licensing and software malfunctions) that disrupted class continuity and reduced benefits, plus suggestions to improve intuitiveness, provide step-by-step guides, ensure student access for autonomous practice, and align tool complexity with prior knowledge. Overall, findings support the tool’s formative potential while underscoring technical and didactic conditions needed to sustain and scale implementation.

The pedagogical integration of artificial intelligence (AI) in engineering education represents a strategic challenge for higher education institutions, particularly in developing contexts where technological, curricular, and faculty-related factors converge. This study analyses the determinants influencing the pedagogical integration of AI in engineering programs at an Ecuadorian university. A quantitative, non-experimental, cross-sectional design was employed with a sample of 87 engineering faculty members. A structured instrument was developed and validated to assess three analytical dimensions: faculty readiness, curricular alignment, and technological conditions. From these, three normalized sub-indices and a Global AI Maturity Index were constructed. Results indicate an advanced level of faculty readiness, a high but still developing level of curricular integration potential, and an emerging level of technological maturity. Multiple linear regression analysis revealed that prior digital experience and basic AI competencies are significant predictors of pedagogical AI integration, while years of teaching experience and institutional support showed no significant effect. These findings suggest that early-stage AI adoption in engineering education is primarily driven by individual faculty capacities rather than institutional technological maturity. The study highlights the need for applied pedagogical training, curriculum redesign, and strengthened technological infrastructure to achieve a more systemic and sustainable integration of AI in higher education

Keywords-- Artificial intelligence in education, engineering education, pedagogical integration, digital competence, higher education innovation