Conference Agenda

The main purpose of this research was to examine how the implementation of challenge-based gamification influences the academic motivation of undergraduate engineering students in Lima, Peru. Using a quantitative methodological approach, the primary technique used was observation, complemented by an evaluation rubric, to measure the effectiveness of this pedagogical strategy on the first variable of the study. Regarding the second variable, corresponding to academic motivation, the survey technique was used, implementing a questionnaire structured into specific dimensions. The study adopted a quasi-experimental design, under the scientific research approach and supported by inductive-deductive logical reasoning. The population consisted of 120 students, developed through census sampling. Finally, according to the results. According to the results of the hypothesis test, it was found that challenge-based gamification generated a significant increase in academic motivation. The T test showed significant differences in the post-test (t = - 13,800, p = 0.000), therefore the null hypothesis was rejected, concluding that at the end of the intervention, a positive and significant effect was obtained on the academic motivation variable.

A web-based system was developed to automate the generation of academic timetables for the Systems Engineering Program at UPTC, integrating student preference data and institutional constraints. Using a heuristic algorithm and a satisfaction matrix derived from a student survey, the system prioritizes preferred time slots while avoiding scheduling conflicts. The backend was built with FastAPI (Python) and the frontend with Vue.js. The tool was validated through comparative analysis against the official timetable, showing improved efficiency, fewer conflicts, and increased alignment with student needs. Results support the adoption of automated scheduling systems in complex academic environments.

The exponential increase in data volume and complexity has made techniques such as Visual Analytics an essential approach for transforming information into actionable insights. However, while visual dashboards are often assumed to accelerate decision-making, there is limited empirical evidence about how different layouts influence users’ cognitive effort and visual attention that will, in the end, trigger knowledge. This study investigates the impact that graphical versus tabular data designs have on decision-making processes, using objective measures from eye-tracking technology. In a controlled environment with 52 participants, we measured two key physiological indicators: pupil dilation, as a proxy for cognitive load, and fixation count, as a measure of visual exploration. Mixed-effects models revealed that, overall, graphical displays did not significantly increase cognitive load, in direct contradiction of the common assumption that visuals inherently reduce mental effort. However, graphical formats prompted a more exploratory mode of attention, evidenced by higher fixation counts and wider dispersed patterns. These findings highlight the importance of aligning data visualization designs with specific analytical tasks, rather than adopting a one-size-fits-all approach. The study contributes to a deeper understanding of how visual environments shape cognitive processes and offers practical recommendations for designing dashboards and decision support tools that are cognitively efficient and fit for purpose.

The integration of computer vision technologies with continuous improvement methodologies has become a key strategy for optimizing safety and efficiency in industrial environments. This study analyzes the combined impact of the YOLO algorithm and the Lean methodology on monitoring the use of PPE and improving operational performance in the manufacturing sector. A systematic methodology based on the PICO and PRISMA guidelines was used, covering publications between 2020 and 2025 extracted from databases such as Scopus, IEEE Xplore, and Web of Science. After a rigorous selection process, 104 relevant studies were analyzed, allowing for the identification of trends, applications, and knowledge gaps. The results show that YOLO, especially in its most recent versions such as YOLOv7, has achieved accuracies of over 95% in real-time PPE detection, surpassing traditional methods such as RFID sensors or classic computer vision algorithms. At the same time, Lean tools such as 5S+1, Kaizen, and Poka-Yoke have proven effective in reducing errors, standardizing processes, and promoting a preventive culture. The combination of both approaches enables more accurate automated monitoring, improves regulatory compliance traceability, and reduces risk exposure. It is concluded that the synergy between YOLO and Lean constitutes an innovative and effective solution for strengthening workplace safety and operational efficiency. However, limitations related to empirical validation in real environments and technological adoption in small businesses are identified, which presents opportunities for future research aimed at scalable and sustainable implementation.

Artificial intelligence (AI) literacy constitutes a priority process for organizations to adapt, compete, and develop in a prospective and strategic manner within a context characterized by the growing influence of AI-driven systems. This process transcends basic technical AI concepts and must be oriented primarily toward ethical and effective use in organizational processes. Organizations have implemented isolated AI training programs, but there is an identified absence of AI literacy frameworks that facilitate planned, organized, disciplined, and strategic processes to generate competitive advantages. The scope of this research is to construct a systematic literature review on artificial intelligence literacy for organizations. The methodological strategy employed consisted of a systematic literature review supported by the PRISMA protocol. AI search engines Bohrium and Undermind were used as information sources to ensure a comprehensive and updated search. The main findings point toward a significant convergence toward structured and multidimensional frameworks for organizational AI literacy, evidencing an evolution from generic approaches toward specific models. The identified frameworks integrate cognitive competencies, practical skills, and socioethical components. The review demonstrates an emerging consensus on the need for contextual differentiation, although limitations persist in longitudinal empirical evaluation of implementations. Practical implications converge on the need for organizations to adopt multidimensional and competency-based AI literacy frameworks, specifically adapted to internal roles and organizational contexts. These frameworks must integrate validated assessment tools and continuous professional development systems to maximize their organizational effectiveness. The review provides structured and multilayered competency frameworks for organizational AI literacy that integrate cognitive foundations, practical skills, and socioethical awareness.

This article presents the notion of NeuroAI Startups, a nascent industry where emotional data supports creative business models. These firms use artificial intelligence, neurotechnology, and biometric emotion tracking to provide substantial solutions in mental health, human resources, education, and marketing, closely associated with the Artificial Intelligence track. By prioritizing Digital Transformation, these firms enable the restructuring of traditional sectors via the incorporation of technology such as brain-computer interfaces, machine learning, and real-time sentiment analysis. Their proficiency in technology is seen in the innovative use of wearables and emotion-sensing devices inside complex social and professional environments. This research analyzes, from a Business Model and Intrapreneurship perspective, the transformation of emotional data into scalable, user-focused solutions that prioritize empathy and customized experiences. These companies use Design Thinking as a core development framework, ensuring that human needs drive innovation processes.

The report highlights Market Impacts, indicating that emotional intelligence is becoming a competitive advantage. By incorporating affective computing into corporate strategy, NeuroAI enterprises are redefining industry standards and client interaction, positioning themselves as pioneers in emotionally intelligent innovation. This study enhances the connection between technology entrepreneurship and emotional and cognitive characteristics, providing strategic insights for firms and education prepared for the future.