Conference Agenda

This study explores undergraduate students' perceptions, uses, and challenges related to artificial intelligence (AI) in academic and entrepreneurial settings. Using a qualitative approach, data were collected from six focus groups (n = 40) and a survey of 125 students in management, engineering, and business programs. Additionally, a systematic review of 68 scientific articles from Scopus (2020–2025) was conducted. Results show that students widely adopt AI for tasks such as text generation, content creation, and entrepreneurial project development. Four key themes emerged: functional academic use, creative idea generation, normative ambiguity, and reduced autonomous thinking. While AI is seen as a tool to boost productivity and innovation, concerns arise regarding ethical uncertainty, overreliance on technology, and limited critical understanding of algorithms. The study highlights the importance of promoting algorithmic literacy and integrating ethical and pedagogical perspectives into higher education. It concludes that AI's benefits can only be fully realized if universities develop clear policies and curricular content to guide responsible and critical use of the technology in both academic and entrepreneurial contexts.

The objective of this study was to analyze which machine learning models best predict mortgage payment difficulties in Peru. A quantitative method was used with longitudinal data from 2018 to 2022 from the National Household Survey (ENAHO), where a total of 5,716 households with mortgage loans were examined. The input variables considered were geographical area, type of housing, use of credit, and source of financing, with difficulty in meeting the payment schedule as the output variable. The analyses were performed in Google Colab, reporting frequency statistics and exploratory and class balancing analyses to evaluate machine learning models such as Logistic Regression, Random Forest (RF), and Support Vector Machine (SVM) with SMOTE. In the training phase of the classification models, the Scikit-learn, XGBoost, and Keras models were trained and compared. The results showed that, of all the models evaluated, Random Forest without adjustments showed the best performance (F1-score = 0.67; recall = 0.71), although combined Stacking (RF + XGBoost) showed a better balance between classes, but its overall performance was lower. In addition, models such as SVM without adjustments show problems in situations of unbalanced classes, highlighting the need to use techniques such as SMOTE. It is concluded that the Random Forest model is more effective in detecting payment difficulties in mortgage loans.

This research examines the integration of disruptive technologies (Artificial Intelligence (AI), blockchain, and Internet of Things (IoT) in the fashion industry's transition to Industry 5.0, employing a mixed-methods approach that combines quantitative analysis of global enterprises, qualitative interviews with stakeholders, and geographical mapping. The main results show that AI's operational benefits (22.7% error reduction, p<0.01) are best when humans and AI work together (28% faster design cycles with "augmented ideation" models). On the other hand, blockchain demonstrates a consumer trust premium (a 67% willingness-to-pay) despite a significant transparency gap (82% demand for ethical sourcing versus 38% recognition of verification, χ²(4) = 38.72, p < 0.001). Geospatial research reveals regional inequalities, indicating that Latin America's adoption lag (38% behind standards) is associated with infrastructure deficiencies (92/100 severity) and colonial legacies (r=0.71, p<0.001). The research enhances theoretical frameworks by augmenting the Technology Acceptance Model with Ethical Perceived Usefulness (β=0.29, p<0.01), which surpasses conventional ease-of-use indicators, and introduces a practical adoption inequality index that contests linear diffusion theories. The results indicate that Small and Medium-sized Enterprises (SMEs) possess an unforeseen edge in agile integration, achieving 24.3% cost savings compared to 17.2% for big enterprises, hence establishing a phase-gated implementation strategy. These results change the definition of Industry 5.0 to mean a human-centered model that needs a balanced mix of technological efficiency (AI's 19.8% cost savings), ethical transparency (blockchain's verification systems), and fair access (targeted policy interventions for emerging markets). This leads to a new framework for sustainable, fair innovation in fashion technology.

The findings reveal that the integration of artificial intelligence (AI) applied to human resource management (HRM) has optimized labor dynamics, increasing both the efficiency and accuracy of processes. In the area of personnel recruitment and selection, AI-based tools have been identified that optimize the search for candidates through data analysis algorithms, reducing time and operating costs. Likewise, with respect to performance evaluation, intelligent systems were identified that allowed a more objective analysis in real time, promoting more informed decision making.

However, this poses important challenges, including ensuring the ethics and transparency of the use of algorithms to avoid bias, as well as the proper training of personnel in the use of these technologies. It was found that technologies that adopt AI in HRM have reported an increase in organizational productivity and an improvement in employee satisfaction. In conclusion, AI is presented as a key ally to transform HRM, enhancing its impact on recruitment and selection of personnel in a successful way, which improves employee satisfaction thanks to the correct process of this area, and also achieves greater retention of talent within industries thanks to the implementation of AI.

This paper describes the design, development and implementation of a mobile application capable of identifying facial skin type (normal, oily, dry) from an image provided by the user. The solution is based on a hybrid system that integrates a convolutional neural network (CNN), used as a feature extractor, and a self-organizing network (SOM), in charge of classifying latent vectors into clusters representative of the skin type. The application architecture combines local processing (image capture and selection, skin tone selection) with remote services hosted in Hugging Face Spaces, accessible through a REST API. The model achieved accuracy levels above 90 % in controlled tests. The mobile implementation in Android Studio with Kotlin allowed to achieve a friendly and functional interface, compatible with modern devices. This approach proves to be an efficient, accessible and scalable alternative for automated dermatological assessment.

The main purpose of the study was to analyze the relationship between information and communication technologies and organizational competitiveness in a funeral services organization. The research has a quantitative, applied approach and correlational scope, based on a non-experimental design. The sample consisted of 143 employees of the company under study. To collect the data, two questionnaires were used, the first one with 22 items intended to measure the ICT variable and the second with 18 items intended to measure the organizational competitiveness variable. Both instruments reported very high reliability, verified through the Cronbach coefficients of 0.928 and 0.918, respectively. The results showed a statistically significant correlation (ρ = 0.672, p < 0.001) between the two study variables, indicating a positive relationship of moderate magnitude. In conclusion, it is inferred that the implementation and use of information and communication technologies will allow organizations to reach important levels of competitiveness.