Search Results (470)

(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed modular framework designed to evaluate reasoning through a structured three-stage process: opinion, counterargument, and synthesis. The framework enables transparent and comparative analysis of how different LLMs handle dialectical reasoning. (2) Methods: Each stage is executed by a single model, and final syntheses are scored via two independent LLM evaluators (LLaMA 3.1 and GPT-4o) based on a rubric with four dimensions: clarity, coherence, originality, and dialecticality. In parallel, a rule-based semantic analyzer detects rhetorical anomalies and ethical values. All outputs and metadata are stored in a Neo4j graph database for structured exploration. (3) Results: The system was applied to four open-weight models (Gemma 7B, Mistral 7B, Dolphin-Mistral, Zephyr 7B) across ten open-ended prompts on ethical, political, and technological topics. The results show consistent stylistic and semantic variation across models, with moderate inter-rater agreement. Semantic diagnostics revealed differences in value expression and rhetorical flaws not captured by rubric scores. (4) Originality: The framework is, to our knowledge, the first to integrate multi-stage reasoning, rubric-based and semantic evaluation, and graph-based storage into a single system. It enables replicable, interpretable, and multidimensional assessment of generative reasoning—supporting researchers, developers, and educators working with LLMs in high-stakes contexts. Full article

In modern medical education, a robust understanding of statistics is essential for fostering critical thinking, informed clinical decision-making, and effective communication. This paper explores the synergistic value of early and continued statistical education for medical students and residents, particularly in relation to the expanding field of sports supplementation and its impact on athletic performance. Early exposure to statistical principles enhances students’ ability to interpret clinical research, avoid cognitive biases, and engage in evidence-based practice. Continued statistical learning throughout residency further refines these competencies, enabling more sophisticated analysis and application of emerging data. The paper also addresses key challenges in integrating statistics into medical curricula—such as limited curricular space, student disengagement, and resource constraints—and proposes solutions including interactive learning, case-based teaching, and the use of public datasets. A unique emphasis is placed on connecting statistical literacy to the interpretation of research in sports science, particularly regarding the efficacy, safety, and ethical considerations of sports supplements. By linking statistical education to a dynamic and relatable domain like sports performance, educators can not only enrich learning outcomes but also foster lasting interest and competence in quantitative reasoning. This integrated approach holds promise for producing more analytically proficient and clinically capable physicians. Full article

This paper examines the epistemological transformation prompted by the integration of generative artificial intelligence technologies into social science research, proposing the “adaptive epistemology” paradigm. In today’s post-digital era—characterized by pervasive infrastructures and non-human agents endowed with generative capabilities—traditional research approaches have become inadequate. Through a critical review of historical and discursive paradigms (positivism, interpretivism, critical realism, pragmatism, transformative paradigms, mixed and digital methods), here I show how the advent of digital platforms and large language models reconfigures the boundaries between data collection, analysis, and interpretation. Employing a theoretical–conceptual framework that draws on sociotechnical systems theory, platform studies, and the philosophy of action, the core features of adaptive epistemology are identified: dynamism, co-construction of meaning between researcher and system, and the capacity to generate methodological solutions in response to rapidly evolving contexts. The findings demonstrate the need for reasoning in terms of an adaptive epistemology that could offer a robust theoretical and methodological framework for guiding social science research in the post-digital society, emphasizing flexibility, reflexivity, and ethical sensitivity in the deployment of generative tools. Full article

Delirium is a common mental health condition encountered in hospitals that increases mortality, hospital length of stay, and healthcare costs. Incarcerated individuals have higher rates of known risk factors for delirium, including mental and physical illness, psychological distress, and stigmatization and may be at increased risk of developing the condition. Despite this, there is a paucity of research in this specific area of psychiatry. We identified ethical concerns, feasibility with the electronic medical record, and stigmatization as reasons why adequate research into this population is limited. Nevertheless, we call on more dedicated research into delirium within the incarcerated population to enhance better care practices and advocate for these patients. Full article

As climate change consequences intensify, higher education institutions (HEIs) have an opportunity and responsibility to model sustainable operations. This study examines how embracing shared knowledge and inclusion in sustainability decision making facilitates green human resource management (GHRM) efforts to invigorate organizational environmental performance. The study examines the effects of shared and inclusive governance on campus sustainability via a regression model and the mediating role of employee participation via a structural equation modeling approach. The results show that shared governance and inclusive governance positively predict the commitment of HEIs to reducing greenhouse gas emissions, and campus engagement mediates these relationships, underscoring the importance of participation. These findings align with stakeholder theory in demonstrating that diverse voices in decision making can enhance commitment to organizational goals like sustainability. The findings also highlight the importance of shared and inclusive governance arrangements at college campuses not only for ethical reasons but also for achieving desired outcomes like carbon neutrality. For campus leaders striving to “green” their institutions, evaluating cross-departmental representation in governance structures and promoting inclusive cultures that make all students and staff feel welcome appear as important complements to GHRM practices. Full article

The COVID-19 pandemic has significantly altered consumer habits, particularly in relation to food choices. In this context, plant-based diets have gained prominence, driven by health, environmental, and ethical considerations. This study investigates the primary motivational and inhibitory factors influencing the consumption of plant-based foods among residents of the Galicia–Northern Portugal Euroregion. Utilizing the Theory of Reasoned Action, an extended model was proposed and tested through a quantitative survey. A total of 214 valid responses were collected via an online questionnaire distributed in Portuguese and Spanish. Linear regression analysis revealed that health awareness, animal welfare, and environmental concern significantly shape positive attitudes, which subsequently affect the intention to consume plant-based foods. Additionally, perceived barriers—such as lack of taste and insufficient information—were found to negatively influence intention. These findings contribute to the consumer behavior literature and provide strategic insights for stakeholders aiming to promote more sustainable dietary patterns in culturally connected cross-border regions. Full article

Evaluating performance is a necessary and specific process across all sectors and organizational levels, shaped by context, indicators, and purpose. Considering global sustainability transitions, understanding financial performance entails a deeper perspective on technical accuracy, conceptual clarity, and systemic integration. This study investigates how financial performance is assessed and interpreted in sustainability-focused research, drawing on a bibliometric analysis of 490 articles indexed in the Web of Science from 2007 to 2023. Using SciMAT, we traced thematic evolutions and revealed a fragmented research landscape marked by competing theoretical, methodological, and practical orientations. To address this conceptual dispersion, we applied the Analytic Hierarchy Process (AHP) to evaluate five key alternatives to financial-performance assessment (quantitative measurement, definition-oriented reasoning, theoretical frameworks, experiential comparison, and integration with sustainability and ethics) against three conceptual criteria (philosophical depth, holistic scope, and multidisciplinary relevance). The results highlight a strong preference for holistic and integrative models of financial performance, with quantitative measurement ranking highest in practical terms, followed by experiential and sustainability-driven approaches. These results underscore the need to align financial evaluation more closely with sustainability values, bridging short-term metrics with long-term societal impact. By combining diachronic thematic mapping with structured decision analysis, this study advances a more reflective and forward-looking framework for performance research. It contributes to sustainability research by identifying underexplored epistemological pathways and supporting the development of financial evaluation models that are inclusive, ethically grounded, and aligned with sustainable development goals. Full article

Objective: There is growing interest in analyzing whether ethical climates influence the emotional states of organizational members. For this reason, the main objective of this study is to evaluate the relationship between a benevolent ethical climate, emotional exhaustion, and depersonalization, taking into account the mediating effect of job autonomy. Methodology: To evaluate the research hypotheses, data were collected from 448 people belonging to six organizations in the Colombian electricity sector. Statistical analysis was performed using two structural equation models (SEMs). Results: The results show that a benevolent climate and its three dimensions (friendship, group interest, and corporate social responsibility) mitigate the negative effect of emotional exhaustion and depersonalization. A work environment focused on people and society triggers positive moods that prevent the loss of valuable psychological resources. On the other hand, job autonomy is a mechanism that has a direct impact on the emotional well-being of employees. Therefore, being able to intentionally direct one’s own sources of energy and motivation prevents an imbalance between resources and demands that blocks the potential effect of emotional exhaustion and depersonalization. Practical implications: This study has important practical implications. First, an ethical climate that seeks to build a caring environment needs to strengthen emotional communication among employees through a high perception of support. Second, organizations need to grow and achieve strategic objectives from a perspective of solidarity. Third, a benevolent ethical climate needs to be nurtured by professionals with a clear vocation for service and a preference for interacting with people. Finally, job autonomy must be accompanied by the necessary time management skills. Social implications: This study highlights the importance to society of an ethical climate based on friendship, group interest, and corporate social responsibility. In a society with a marked tendency to disengage from collective problems, it is essential to make decisions that take into account the well-being of others. Originality/value: This research responds to recent calls for more studies to identify organizational contexts capable of mitigating the negative effects of emotional exhaustion and depersonalization. Full article

The growing recognition of animal sentience has led to notable progress in European Union animal welfare legislation. However, a significant inconsistency remains: while mammals, birds, and cephalopods are legally protected as sentient beings, honeybees (Apis mellifera)—despite robust scientific evidence of their cognitive, emotional, and sensory complexity—are excluded from such protections. This manuscript examines, from an interdisciplinary perspective, the divergence between emerging evidence on invertebrate sentience and current EU legal frameworks. Honeybees and cephalopods serve as comparative case studies to assess inconsistencies in the criteria for legal recognition of sentience. Findings increasingly confirm that honeybees exhibit advanced cognitive functions, emotional states, and behavioral flexibility comparable to those of legally protected vertebrates. Their omission from welfare legislation lacks scientific justification and raises ethical and ecological concerns, especially given their central role in pollination and ecosystem stability. In general, we advocate for the inclusion of Apis mellifera in EU animal welfare policy. However, we are aware that there are also critical views on their introduction, which we address in a dedicated paragraph of the manuscript. For this reason, we advocate a gradual and evidence-based approach, guided by a permanent observatory, which could ensure that legislation evolves in parallel with scientific understanding, promoting ethical consistency, sustainable agriculture, and integrated health under the One Health framework. This approach would meet the concerns of consumers who consider well-being and respect for the environment as essential principles of breeding, and who carefully choose products from animals raised with systems that respect welfare, with indisputable economic advantages for the beekeeper. Full article

Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework. Full article

Large Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growing adoption, current research often focuses on isolated applications, lacking a systematic understanding of how LLMs align with domain-specific requirements and pedagogical effectiveness. This highlights a pressing need for comprehensive evaluations that address the challenges of integration, generalization, and ethical deployment in both operational and educational cyber security environments. Therefore, this paper provides a comprehensive and State-of-the-Art review of the significant role of LLMs in cyber security, addressing both operational and educational dimensions. It introduces a holistic framework that categorizes LLM applications into six key cyber security domains, examining each in depth to demonstrate their impact on automation, context-aware reasoning, and adaptability to emerging threats. The paper highlights the potential of LLMs to enhance operational performance and educational effectiveness while also exploring emerging technical, ethical, and security challenges. The paper also uniquely addresses the underexamined area of LLMs in cyber security education by reviewing recent studies and illustrating how these models support personalized learning, hands-on training, and awareness initiatives. The key findings reveal that while LLMs offer significant potential in automating tasks and enabling personalized learning, challenges remain in model generalization, ethical deployment, and production readiness. Finally, the paper discusses open issues and future research directions for the application of LLMs in both operational and educational contexts. This paper serves as a valuable reference for researchers, educators, and practitioners aiming to develop intelligent, adaptive, scalable, and ethically responsible LLM-based cyber security solutions. Full article

When nurses encounter people in institutional settings who are living with dementia and self-neglecting their hygiene, they are challenged to provide care that respects autonomy while upholding the ethical principles of beneficence and non-maleficence. This study aimed to understand how nurses respond when confronted with patients who decline assistance with personal hygiene and then became physically aggressive. Methods: A qualitative descriptive study employing think-aloud interviewing to explore nurses’ clinical reasoning about how they would proceed with the care of a patient living with dementia who self-neglects their hygiene. Results: Nurses describe many creative ways that they would work with patients to accomplish personal hygiene care in an ideal world. Participants also share the many barriers they experience to providing desired care and instances where they would force care with people who self-neglect their hygiene. Thematic analysis revealed six key themes: non-preferred approaches to care; preferred approaches to care; barriers to actualizing preferred approaches; responding to continued resistance to care; justified use of force; and efforts to minimize harm. Conclusions: This study highlights that ethical nursing practice in dementia care is not simply a matter of following through with best practices. It is an ongoing negotiation, carried out in environments that are often misaligned with nurses’ values. Full article

Critical thinking (CT) is widely recognised as a key competency in education for sustainable development (ESD). However, international research shows that many teachers feel unprepared to teach CT, especially within the ESD context. Despite its importance, few studies have explored how CT is actually practiced in ESD, particularly in primary and secondary education. This article presents a systematic literature review of 43 qualitative studies published between 1990 and 2021, following the PRISMA guidelines. This review aimed to (1) synthesise research on teachers’ understanding of CT in ESD and (2) identify teaching practices where CT is integrated into ESD. The findings reveal that the concept of CT is frequently used but is not clearly understood in the context of ESD. Most studies focused on critical rationality (skills), with fewer addressing critical character (dispositions), critical actions, critical virtue, critical consciousness, or critical pedagogy. This review highlights a need for broader engagement with these dimensions in order to foster ethically aware and responsible citizens. We argue for teaching approaches that involve students in interdisciplinary, real-world problems requiring not only critical reasoning but also action, reflection, and ethical judgment. Full article

This paper proposes a technological framework designed to mitigate the inherent risks associated with the deployment of artificial intelligence (AI) in decision-making and task execution within the management processes. The Agreement Validation Interface (AVI) functions as a modular Application Programming Interface (API) Gateway positioned between user applications and LLMs. This gateway architecture is designed to be LLM-agnostic, meaning it can operate with various underlying LLMs without requiring specific modifications for each model. This universality is achieved by standardizing the interface for requests and responses and applying a consistent set of validation and enhancement processes irrespective of the chosen LLM provider, thus offering a consistent governance layer across a diverse LLM ecosystem. AVI facilitates the orchestration of multiple AI subcomponents for input–output validation, response evaluation, and contextual reasoning, thereby enabling real-time, bidirectional filtering of user interactions. A proof-of-concept (PoC) implementation of AVI was developed and rigorously evaluated using industry-standard benchmarks. The system was tested for its effectiveness in mitigating adversarial prompts, reducing toxic outputs, detecting personally identifiable information (PII), and enhancing factual consistency. The results demonstrated that AVI reduced successful fast injection attacks by 82%, decreased toxic content generation by 75%, and achieved high PII detection performance (F1-score ≈ 0.95). Furthermore, the contextual reasoning module significantly improved the neutrality and factual validity of model outputs. Although the integration of AVI introduced a moderate increase in latency, the overall framework effectively enhanced the reliability, safety, and interpretability of LLM-driven applications. AVI provides a scalable and adaptable architectural template for the responsible deployment of generative AI in high-stakes domains such as finance, healthcare, and education, promoting safer and more ethical use of AI technologies. Full article

This study proposes a conceptual model that explains the influence of leadership on the relationship between human intelligence (HI) and artificial intelligence (AI). A qualitative, non-systematic literature review was conducted in Scopus and Web of Science of the literature published in the last 5 years, using Boolean combinations of the terms “leadership,” “artificial intelligence,” and “human intelligence.” The thematic analysis allowed the identification of conceptual patterns and research gaps; the model elaborated from the review shows that leadership has an ethical and strategic mediation in the HI-AI relationship in a hybrid space of cooperation, in which automated decisions are put in real context through human judgment and reasoning; ethical governance mechanisms emerge for systems supported by artificial intelligence; and finally, a balancing mechanism to algorithmic efficiency is established through cognitive adaptability. The proposed framework offers organizations some guidelines for human supervision processes for AI-supported systems that integrate ethical evaluations into automated processes. It proposes elements—leadership tools that enhance the relationship between human intelligence and artificial intelligence. This article contributes to the management of organizations by proposing a model that recognizes leadership as a dynamic facilitator between HI and AI, integrating transdisciplinary knowledge of management, technological ethics, and cognitive science, and proposing an ethical interrelationship in the decision-making architectures between HI and AI. The proposed model establishes leadership mediation of human–AI interaction through four axes showing how leadership acts as the axis that brings together human and technological systems to work together. Hierarchical interaction creates a hybrid interaction that is highly flexible, efficient, and has ethical oversight. Finally, the proposed model is an open system that interacts with the environment and is understood as a flexible tool to support strategic decision-making in complex environments. Full article