EthAIca (Journal)
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Characterization of the Use of Artificial Intelligence Tools among Students at the University of Medical Sciences of Santiago de Cuba
Get PDFIntroduction: since its inception, the field of artificial intelligence has evolved significantly, transitioning from a mere academic curiosity to becoming an essential tool in multiple sectors, including medicine.Objective: To characterize the use of Artificial Intelligence tools among students at the University of Medical Sciences of Santiago de Cuba.Methods: an observational, descriptive, and cross-sectional study was conducted at the University of Medical Sciences in Santiago de Cuba from April to June 2025. The study population consisted of the university students, and a representative sample of 1,050 was obtained through stratified random sampling, ensuring the inclusion of students from different academic years who met the inclusion criteria. Results: the most represented major was Medicine (59.52%). A medium level of knowledge about the definition of artificial intelligence predominated (51,14 %), and the most commonly used AI tool was OpenAI ChatGPT (100 %). There was a notable positive perception of these tools (84,19 %), and 91,71 % of respondents confirmed having a positive impact from their use. Conclusions: understanding the impact of artificial intelligence on medical education is highly relevant for preparing future professionals for an increasingly digitalized and data-driven work environment.
Teachers’ Perceptions of Learning From, About, and With Artificial Intelligence in Education (AIED): Implications for Ethical Practice and the Challenges of AI Use in Basic Education
Get PDFTeachers’ perceptions play a key role in shaping how emerging technologies are accepted and applied in education. With artificial intelligence (AI) becoming more prominent in schools, it is important to explore how teachers view its role as a source of knowledge, a subject to be taught, and a tool for instruction. The purpose of this study was to examine teachers’ perceptions across three domains—learning from AI, learning about AI, and learning with AI—and to analyze how these areas are interrelated. A descriptive-quantitative-correlational design was employed, involving 204 public elementary teachers selected through proportionate random sampling from 22 schools in Manicahan District, Division of Zamboanga City. Results revealed that teachers expressed high perceptions of learning from AI (M = 4,36, SD = 0,58) and learning about AI (M = 4.22, SD = 0.62), while learning with AI received a very high rating (M = 4,48, SD = 0,55). The overall mean score of 4,35 (SD = 0,58) indicated generally favorable views toward AI in education. Correlation analysis further showed significant positive relationships among the three domains, with the strongest link between learning about AI and learning with AI (r = 0,546, p < 0,001). These findings suggest that as teachers deepen their knowledge of AI, they are more inclined to apply it in classroom practice, highlighting the importance of professional development that integrates both conceptual understanding and practical application of AI in teaching
Artificial intelligence in health care management: ethical challenges, benefits and opportunities
Get PDFIntroduction: artificial intelligence is transforming healthcare management by improving the efficiency and personalization of care. Therefore, the objective was to describe how artificial intelligence is integrated into healthcare management, as well as the ethical challenges that exist in its application and the administrative opportunities it offers.Method: a qualitative, descriptive and literature review type study, 30 articles were analyzed, selected by convenience sampling according to the fulfillment of eligibility criteria. The information was obtained from scientific databases such as SCOPUS, SCIELO, and PUBMED, and was organized in an Excel matrix for subsequent evaluation using the CASPe method. Development: Artificial intelligence in healthcare offers benefits such as efficiency, personalization of care and improved diagnoses. However, it faces ethical challenges such as privacy, lack of regulation and risk of dehumanization. Its implementation requires investment, a clear legal framework and an ethical approach. Conclusion: t optimizes the management of health services through data analysis and medical decision support, although its adoption presents challenges such as the absence of regulations, high costs, unequal access and ethical concerns. Therefore, it is essential to develop regulations and protocols that guarantee a fair and responsible use in its application in the quality of care. Keywords: Health management; Artificial intelligence; Health services
Artificial Intelligence-Driven Smart Aquaculture: Revolutionizing Sustainability through Automation and Machine Learning
Get PDFAI incorporation in aquaculture has transformed the industry completely, making crucial processes automated, maximizing productivity, and promoting sustainability. AI, specifically machine learning, refers to the application of modern smart aquaculture systems for tasks such as fish species classification, health monitoring, feed regulation, and management of water quality. It thereby sets inefficiency issues right while reducing impacts on the environment through real-time data-driven decision-making. This article deals with very recent developments in the applications of AI and machine learning in aquaculture, pointing out their importance in increasing production as well as eco-friendly management of aquatic environments
Digital Transformation and the Changing Nature of Work: Emerging Challenges
Get PDFDigital technologies are maturing and working rapidly to change the work environment of today, disrupting age-old job structures, organizational practices, and the very nature of work. This study aims to address the ways in which digital transformation, propelled by technologies such as AI, automation, cloud computing, and big data, is redefining work across all industries. With digital tools in vogue, employees need new roles, higher-level digital competencies, and alternative work arrangements like remote and hybrid models. These transformations are creating new challenges that include technostress, job obsolescence, and digital inequality.Methods:A qualitative study informed by very recent empirical studies and theoretical literature. The approach is to synthesize insights from diverse case studies, industry-specific labor reports, and academic research into the impact of digital transformation on the workforce and organizational behavior.Results:Such findings reveal the very significant impacts of digital transformation on employee productivity, work-related satisfaction, and the culture of the organization. The importance of digital leadership, strategic change management, and continuous upgrading of employee skills significantly applies in their effective management of the transformation process.Conclusions:Going forward, organizations should focus on creating a resilient workforce that is digitally agile to remain competitively viable in a rapidly changing socio-economic landscape. In this context, the paper not only outlines opportunities and challenges associated with the changing nature of work but also presents strategic opportunities for policymakers, educators, and business leaders to capitalize on digital transformation.
The risk of moral outsourcing: why artificial intelligence cannot and should not make our ethical decisions
Get PDFIntroduction: The delegation of ethical decision-making to artificial intelligence (AI), a practice termed \u27moral outsourcing,\u27 was examined. Objective: This paper critically analyzes the philosophical and social implications of moral outsourcing to AI. Method: A philosophical and theoretical analysis was conducted, synthesizing arguments from ontology, ethics, and social theory. Results: The analysis revealed three core arguments against this practice. First, an ontological gap was identified; AI systems lacked the consciousness and subjective experience necessary for genuine moral agency. Second, the study found that the rich, contextual nature of human ethics could not be successfully reduced to formal logic without mechanizing historical biases and losing essential meaning. Third, it was argued that moral outsourcing would lead to an atrophy of human moral reasoning skills and an erosion of accountability. Conclusions: It was concluded that AI should be developed as a tool to augment, not replace, human judgment, and that the final authority for ethical choice must remain a fundamentally human responsibility
Perceptions of Generative AI among Development Communication Students: Insights by Gender and Age from the Philippines
Get PDFGenerative artificial intelligence (GenAI) tools such as ChatGPT are increasingly used in higher education, yet students’ perceptions remain varied and may be shaped by demographic factors. This study examined the overall perceptions of Development Communication students toward generative AI and investigated whether these perceptions differ by gender and age. Using a descriptive-quantitative design, survey data were collected from 208 students and analyzed using descriptive statistics and independent samples t-tests. The results showed a neutral overall perception of generative AI (M = 3,31; SD = 0,65), indicating a balanced view of its advantages and limitations. Students positively rated AI’s 24/7 availability (M = 3,46; SD = 0,97), its ability to offer unique perspectives (M = 3,42; SD = 1,00), and teachers’ growing awareness of AI-assisted work (M = 3,63; SD = 0,82). Skepticism was evident regarding AI’s potential to replace teachers (M = 2,86; SD = 1,20). A significant gender difference emerged, with male students (M = 3,81; SD = 0,28) reporting higher perceptions than female students (M = 3,07; SD = 0,65), t(206) = 8,94; p < 0,001; d = 0,55. No significant differences were found across age groups, t(206) = –0,52; p = 0,61. Overall, the findings suggest that students recognize the usefulness of generative AI but remain cautious about its limitations and ethical implications. The observed gender disparity underscores the need for inclusive AI literacy initiatives to support equitable and responsible integration of GenAI in higher education
Digital transformation in SMEs: challenges and potential in the data age
Get PDFThe study investigated the degree of knowledge and use of Big Data and Artificial Intelligence (AI) tools in small and medium-sized enterprises (SMEs) in the province of Córdoba. It analysed how the fourth industrial revolution transformed business models through the integration of new technologies, highlighting that the speed of adoption was decisive. It was observed that many SMEs mistakenly assumed that these tools were reserved for large companies, without considering the hidden value in the data they already generated. The work identified that Big Data allowed massive information to be processed in real time, improving customer service, logistics, and personalisation of services, as was the case of companies such as Netflix, Amazon and Tesla. The analysis revealed that while 75% of SMEs considered technology incorporation important, only 32% really understood AI and 34% were aware of Big Data. The main barriers included lack of investment and the need for training of human talent.Finally, it was concluded that SMEs had to understand that the strategic use of data did not depend on the size of the company, but on their ability to connect and leverage the available information, which represented a decisive opportunity for their sustainability and competitiveness
Predictive Analytics in Education: Modeling the Complex Relationship Between Learning Modalities and Student Well-being
Get PDFIntroduction: This study examines publication trends related to student stress and mental health during online learning periods, while exploring opportunities for curriculum innovation in post-pandemic education. Method; The research utilizes a Kaggle-sourced dataset comprising survey responses from 1,000 students about their psychological well-being during remote education. This rich dataset includes ten variables capturing demographic information, lifestyle patterns, and self-assessed mental health metrics, providing valuable material for comprehensive data exploration, visualization, and predictive analysis of digital learning\u27s psychological impacts. Beyond immediate stress assessment, the data enables investigation of broader themes including educational technology adaptation, sleep disruption, social isolation, stress perception, and emotional coping mechanisms. Result: The findings highlight the urgent need for educational systems to develop flexible curricula that address both pandemic-era challenges and evolving post-COVID learning environments. Conclusion: The study proposes curriculum frameworks that integrate mental health support with academic content, preparing institutions for future disruptions while promoting student resilience in hybrid learning settings
Artificial Intelligence in health Education: Opportunities, Ethical Constraints and Pedagogical Challenges
Get PDFIntroduction: artificial intelligence (AI) has great potential to transform healthcare and health education. Therefore, we set out to analyze the scientific evidence on the implementation of artificial intelligence in the training of healthcare professionals, considering the ethical limitations of its application and opportunities for competence development. Method: the research had a qualitative approach, with a descriptive design and a literature review. Eighty-six articles were analyzed, obtained from indexed databases such as Scopus, Web of Science, Redalyc, and Scielo, using keywords in English, Portuguese, and Spanish. The information was organized in an Excel matrix for analysis through critical reading using the CASPe method to evaluate the scientific quality of the studies.Results: the implementation of AI in health education improves personalized learning, clinical simulations, and decision-making through data analysis. However, its use carries risks such as the dehumanization of the training process and excessive dependence on technology. Therefore, it is necessary to train teachers and establish ethical limits that ensure a balance between innovation and critical thinking. Conclusión: artificial intelligence is revolutionizing health education by improving learning and clinical skills. However, it poses ethical challenges that require regulation and responsible use. Its successful integration requires balancing technology and the inclusion of human values