EthAIca (Journal)
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Application of artificial intelligence in the field of legal and forensic medicine: advances and future challenges
Get PDFIntroduction: Artificial intelligence (AI) has emerged as a profoundly transformative tool in numerous fields of knowledge, and its application in legal and forensic medicine is opening a new chapter in forensic science. The development of the “JL-IDIF” system by the Forensic Research Institute (IDIF) has been recognized as an innovative step, setting precedents for the use of advanced technology for the recording and analysis of forensic data. AI represents an unprecedented opportunity to transform legal and forensic medicine, making these processes faster, more efficient, and more accurate. Methodology. An information search was conducted from January to May 2025. Information was collected from scientific articles, books, technical reports, and publications in specialized media, using databases such as PubMed, Scopus, Google Scholar, and websites of forensic and government institutions. This approach allowed for a comprehensive and well-founded synthesis of the available information. Conclusions. The emergence of artificial intelligence (AI) has transformed multiple areas of medicine, and its incursion into forensic and legal medicine marks the beginning of a new era in forensic practice. This review has shown that, while technological advances have demonstrated great potential, significant limitations remain related to data quality, the need for external validation, and the availability of adequate technological infrastructure. In Bolivia, initiatives such as the JL-IDIF project or the experimental implementation of generative AI models demonstrate the interest and initial capacity to explore these emerging technologies. AI should not be viewed as a substitute for human judgment, but rather as a powerful tool that enhances the work of experts, allowing them to focus on critical interpretation and decision-making
The Impact of AI-Based Learning on Academic Performance
Get PDFThis study compellingly demonstrates the effectiveness of AI-driven personalised learning algorithms in boosting academic performance among secondary school students in Portugal. Using a rigorous quasi-experimental, non-randomised two-shot pre-test and post-test design, we engaged sixty 10th-grade students divided into two distinct groups. The experimental group experienced AI-assisted instruction through innovative platforms, including Brisk Teaching, Khanmigo, ChatGPT 4.0 Turbo, and Quizizz AI, while the control group adhered to traditional teaching methods. Both groups participated in identical pre-tests and post-tests for two essential units: Energy in the Ecosystem and Heredity and Variation.Robust statistical analyses, including paired and independent samples t-tests, revealed significantly greater learning gains in the AI-driven group compared to the control group. Moreover, we assessed the influence of key factors, including student engagement, prior knowledge, and learning preferences, using validated Likert-scale questionnaires. The results clearly indicated a strong positive correlation between AI-driven learning and enhanced student motivation and comprehension. These findings strongly support the use of AI-based personalised instruction as an effective strategy for enhancing learning outcomes in STEM education, particularly in diverse classroom settings.
Notes on the Governance, Regulation, and Public Policy of Artificial Intelligence
Get PDFArtificial intelligence (AI) is reshaping economic, social, and political systems at an unprecedented pace, generating transformative opportunities alongside ethical, legal, and geopolitical risks. This article explores key international trends in AI governance and regulation, emphasizing multilevel approaches that integrate local, national, and global dimensions. Through an analysis of hard and soft law frameworks, public–private cooperation initiatives, and institutional developments across Europe, North America, Asia-Pacific, and Latin America, the study identifies critical factors for effective governance: shared ethical principles, adaptive regulatory structures, and accountability mechanisms. It concludes that AI governance must transcend risk mitigation and serve as a guiding compass for democratic, inclusive, and sustainable digital transformation
Ethical Guidelines for the thoughtful Implementation of AI in Higher Education
Get PDFAs artificial intelligence (AI) integrates into education systems, concerns regarding its ethics become magnified. This chapter addresses the need for ethical frameworks on AI applications regarding the basic values of education: equity, transparency, and accountability. With rapid AI expansion in teaching and learning, systemic bias, student privacy, and stakeholder responsibilities emerge as burning issues
AI: Challenges for contemporary digital education
Get PDFIntroduction: This study analyzes the challenges of artificial intelligence (AI) in digital education between 2019 and 2022, using a bibliometric approach. The research arose from the need to systematize existing knowledge and guide future lines of work in this emerging field. Methodology: A search was conducted in Scopus, Web of Science, Google Scholar, and ERIC, using terms such as "AI," "digital education," and "challenges." The data was filtered by year, language, and document type, and processed with tools such as VOSviewer and Bibliometrix to analyze productivity, collaborations, and thematic trends. Results: Key authors and institutions, collaborative networks, and recurring themes, such as ethics, adaptive learning, and teacher training, were identified. Scientific production showed steady growth, with a predominance of publications in English. Conclusions: The study highlights the main challenges of AI in digital education and highlights the need to investigate its ethical and pedagogical impact. The methodology employed provides a basis for future reviews
A comprehensive exploration of nongeostationary satellite systems in the mining industry: emphasizing AI , ethical considerations, and communication strategies
Get PDFNon-geostationary satellite (NGSO) constellations—particularly LEO/MEO—are transforming mining by providing low-latency connectivity and taskable Earth observation to remote, infrastructure-poor sites. Objectives include mapping NGSO applications across exploration, planning, and operations; assessing AI\u27s role in tasking, routing, and analytics; and examining governance and ESG implications, with a focus on Africa and East Africa. Methods involved a PRISMA-aligned systematic review (protocol registered) synthesising primary and secondary evidence on NGSO-enabled EO and communications in mining. A random-effects meta-analysis was planned if three or more comparable studies reported the same outcome; otherwise, a structured narrative synthesis with predefined subgroups (LEO vs MEO, EO vs backhaul, open-pit vs underground, Africa vs elsewhere) was used. Results and discussion showed that across more than 30 use cases, NGSO backhaul and EO tasking consistently reduced time to insight for pit progression, tailings surveillance, and asset tracking; simulations indicated routing improvements of approximately 10% on tree topologies and 30% on mesh networks at N=500, demonstrating tangible latency and capacity benefits for safety-critical workflows. Continuity was enhanced through multi-sensor PNT (GNSS/inertial/vision plus radio localisation ) and hierarchical link adaptation that rapidly re- parameterises under noise, weather, or interference. AI added value by improving tasking and congestion control in edge and cloud inference, though it required cascaded models, compression, and uncertainty gating to meet compute and bandwidth constraints. Governance themes—such as data protection, transparency, and community benefit—were recurring enablers of adoption. Conclusion: When combined with resilient positioning, adaptive operations, and credible ESG safeguards, NGSO combined with AI can significantly enhance mining efficiency, safety, and sustainability; priorities include standardised KPIs, transparent cost models, and long-term pilot deployments
ChatGPT in the Academic Sphere: Teacher Aspirants’ Perceptions of Privacy and Security Across Education Career Programs
Get PDFThe integration of artificial intelligence (AI) into education has raised questions about privacy, security, and ethical use, particularly with tools such as ChatGPT. While prior research has focused primarily on students’ adoption, limited attention has been given to teacher aspirants’ perceptions across education career programs, leaving a gap in understanding future educators’ readiness to engage with AI. This study aimed to determine the perceived privacy and security of ChatGPT among teacher aspirants and to examine whether significant differences exist across programs in teacher education. A descriptive–comparative quantitative design was employed, involving 150 respondents enrolled in the Bachelor in Elementary Education (BEED), Bachelor in Secondary Education (BSED), Bachelor in Special Needs Education (BSNED), Bachelor in Early Childhood Education (BECED), and Bachelor in Culture and Arts Education (BCAED) programs. Data were collected through a structured online questionnaire with 14 items on a five-point Likert scale and analyzed via descriptive statistics and one-way ANOVA. The results revealed generally positive perceptions of ChatGPT’s privacy (M = 3,44, SD = 0,84) and security (M = 3,42, SD = 0,83). However, uncertainty persisted regarding the safety of sharing personal information. No significant differences were observed across the five programs, indicating shared perceptions regardless of disciplinary background. Notably, consistent with national trends, teacher education remains dominated by female students. The study concludes that while teacher aspirants recognize ChatGPT’s benefits, concerns about data privacy and security persist. It is recommended that teacher education programs integrate AI literacy training, with emphasis on data ethics, transparency, and responsible usage, to prepare future educators as both confident and cautious technology users
Interplay of AI Literacy, Readiness-Confidence, and Acceptance among Pre-Service Teachers in Philippine Higher Education: A Gender, Discipline, and Connectivity Perspective
Get PDFThis study explores the role of artificial intelligence (AI) in teacher education, focusing on preservice teachers’ preparedness for AI integration. It examined the levels of AI literacy, readiness-confidence, and acceptance among preservice teachers in Philippine higher education institutions, and investigated differences across gender, academic discipline, and internet connectivity. Using a cross-sectional survey design, data were collected from 384 preservice teachers through validated instruments that measured AI literacy, readiness-confidence, and acceptance. Analyses included descriptive statistics, independent samples t-tests, and correlation analysis. Findings revealed high readiness-confidence and moderate to high literacy and acceptance levels. Significant differences emerged, with male preservice teachers, STEM students, and those with reliable internet access reporting higher scores, particularly in readiness-confidence. Strong positive correlations among literacy, readiness-confidence, and acceptance underscored their interdependent relationship in shaping preparedness for AI integration. These results emphasize the need for tailored and inclusive AI education and training programs that address demographic and infrastructural disparities. Beyond equipping preservice teachers with skills, preparing them for AI adoption is about shaping the future of education by ensuring that tomorrow’s classrooms are led by educators who are competent, confident, and capable of driving innovation, equity, and progress in a rapidly evolving digital age.
Epistemological approach to bibliometric analysis
Get PDFThe present article proposes an epistemological approach to bibliometrics, understood not only as a set of quantitative techniques to measure scientific production, but also as a practice loaded with ontological, political and social assumptions that shape the field of knowledge. Through an analytical-interpretative approach, the paradigms that have underpinned its development are critically reviewed, from the positivist and functionalist tradition to constructivist and critical perspectives. The study argues that bibliometrics operates as an evaluation technology that not only reflects science, but also shapes it, establishing normative criteria that condition the production, circulation and legitimization of knowledge. In view of its epistemic limitations and its role in the reproduction of cognitive inequalities, we propose a re-reading from a constructivist-critical paradigm, capable of integrating ethical, contextual and pluralistic dimensions in the evaluation of scientific knowledge. This reflection seeks to contribute to a more just, situated and socially relevant model of science
Epistemic Injustice in Generative AI: A Pipeline Taxonomy, Empirical Hypotheses, and Stage-Matched Governance
Get PDFIntroduction: generative AI systems increasingly influence whose knowledge is represented, how meaning is framed, and who benefits from information. However, these systems frequently perpetuate epistemic injustices—structural harms that compromise the credibility, intelligibility, and visibility of marginalized communities.Objective: this study aims to systematically analyze how epistemic injustices emerge across the generative AI pipeline and to propose a framework for diagnosing, testing, and mitigating these harms through targeted design and governance strategies.Method: a mutually exclusive and collectively exhaustive (MECE) taxonomy is developed to map testimonial, hermeneutical, and distributive injustices onto four development stages: data collection, model training, inference, and dissemination. Building on this framework, four theory-driven hypotheses (H1–H4) are formulated to connect design decisions to measurable epistemic harms. Two hypotheses—concerning role-calibrated explanations (H3) and opacity-induced deference (H4)—are empirically tested through a PRISMA-style meta-synthesis of 21 behavioral studies.Results: findings reveal that AI opacity significantly increases deference to system outputs (effect size d ≈ 0,46–0,58), reinforcing authority biases. In contrast, explanations aligned with stakeholder roles enhance perceived trustworthiness and fairness (d ≈ 0,40–0,84). These effects demonstrate the material impact of design choices on epistemic outcomes.Conclusions:Epistemic justice should not be treated as a post hoc ethical concern but as a designable, auditable property of AI systems. We propose stage-specific governance interventions—such as participatory data audits, semantic drift monitoring, and role-sensitive explanation regimes—to embed justice across the pipeline. This framework supports the development of more accountable, inclusive generative AI