Conference Agenda

Abstract– Artificial intelligence (AI) is rapidly transforming various fields, including education, by offering new tools that can revolutionize teaching and learning. This article analyzes attitudes toward AI, as well as the challenges and opportunities it presents. As AI expands, its use in education has generated both hope and concern, necessitating regulations to govern its application, especially in higher education. Methods, this study focuses on adapting the SATAI (Student Attitude Toward Artificial Intelligence) scale in a Honduran context, using an incidental sample of 217 university students from the Central American Technological University (UNITEC). The study follows a quantitative, cross-sectional, and non-experimental approach, aiming to explain the relationship between students' attitudes toward AI and their cognitive, affective, and behavioral characteristics. The results include an adaptation of the SATAI scale, with a reliability score of 0.88 and a KMO of 0.89, proposing a reduced 16-item version that demonstrated good performance based on goodness-of-fit statistics. Findings indicate a favorable attitude toward AI (3.6 out of 5), particularly in academic and entertainment contexts, highlighting the importance of teacher training for the ethical and effective use of these technologies. The discussion emphasizes the need for continued research on the relationship between AI and education, considering the potential long-term ethical and moral implications. This study contributes to the understanding of how AI is integrated into educational settings.

The overall objective of the research was to build an artificial neural network model to evaluate rock mass stability in surface mining. Different optimizers are examined, initially highlighting Adam and RMSprop, for their capabilities to dynamically adapt the learning rate. Adam is noted as particularly advantageous in deep networks, while RMSprop is effective in data with high variability. However, SGD (Stochastic Gradient Descent) is found to be the most suitable optimizer, due to its simplicity and stability, which reduces the risk of overfitting in noisy data. The research emphasizes the slower convergence of SGD, which, when configured with momentum, improves its ability to avoid local minima. The database used in this study contains information on rock mass geomechanics and structural parameters that affect slope stability. Critical variables include structural domain, inclination and direction of rock structures, along with measures of cohesion and strength. These parameters are critical for tuning the model and achieving greater generalization in the validation set. In summary, the paper presents a detailed evaluation of optimization methods and the importance of a careful approach in handling geotechnical data

The implementation of Enterprise Resource Planning (ERP) systems has transformed business management by integrating data and optimizing processes. This review aims to analyze the impact of ERPs on improving operational efficiency, the technological tools employed, and the limitations identified before and after their implementation. A systematic literature review was conducted using only the Scopus database, selecting 27 articles published between 2021 and 2024 based on PRISMA criteria. ERPs stand out for enhancing information quality, integrating technologies such as AI and data analytics, and promoting sustainable practices. However, challenges such as organizational resistance to change and infrastructure limitations hinder their effectiveness. It is concluded that ERPs are essential for digital transformation, especially in SMEs, as they drive modernization and sustainability. Future studies should explore emerging technologies and sustainable approaches to maximize their impact on businesses

Construction companies are responsible for managing safety within their facilities and work shifts. The use of artificial vision (AV) in civil construction has provided project executors with numerous benefits and opportunities, including extensive data collection, sustainable evaluations, and productivity improvements. The shift toward sustainability in construction is increasingly supported by digital technologies. In this context, this article reviews the literature to analyze the influence of AI in civil engineering. According to findings, the publication trend peaked among researchers in 2020. Risk management in construction is crucial for maintaining a safe work environment free from threats that could harm both project progress and worker productivity. However, this aspect often receives insufficient attention, partly due to the reliance on traditional risk identification methods, which can be inefficient or slow. For this reason, this study aims to automate the risk management process by identifying and counting such situations in structural tasks in the city of Lima, emphasizing activities involving height risks or falling objects. The methodology followed includes: (A) data collection and analysis through expert judgment, (B) assessment of the traditional risk identification process across four evaluated projects, (C) development of an automated risk identification process using artificial vision, and (D) implementation of this process. The results demonstrate an increase in risk situation identification and improved evaluation of the causes behind these risks for subsequent mitigation. The main conclusion is that artificial vision technology automates the risk identification process, enabling real-time detection and significantly reducing the time compared to traditional methods.

The purpose of this research is to analyze the impact and effectiveness of artificial intelligence (AI) models in diagnosing and predicting leukemia, with a particular focus on acute myeloid leukemia (AML). The goal is to identify the most accurate and robust models, as well as their limitations and medical applications. To this end, a systematic literature review (RSL) was carried out using the PRISMA methodology; the search was carried out in the Scopus database, identifying a total of 429 articles related to leukemia, and multiple cases of AML were analyzed that were evaluated with various AI approaches, including deep learning and machine learning. The models were evaluated based on their accuracy and the quality of the data used, with an emphasis on techniques such as convolutional neural networks (CNN), XGBoost, and hybrid models. The results show that the weighted convolutional neural network (WVCNN) model achieved 99.9% accuracy by analyzing genomic data. Techniques such as XGBoost and ResNet-50 demonstrated high effectiveness in different fields, achieving accuracy rates of 89% and 86%, respectively, depending on the type of data analyzed, whether tabular or medical images. In conclusion, AI advances have revolutionized AML prediction by combining hybrid approaches to overcome current limitations. Finally, it is suggested that future research should focus on the integration of multiple techniques, as well as the development of more interpretable models.