Search Results (64)

Background/Objectives: Artificial intelligence reshapes clinical practice, and its effect on the clinician–patient relationship requires reconsideration of the frameworks that have shaped modern medical ethics. When clinicians delegate expertise to algorithms they cannot verify, it becomes unclear who bears clinical responsibility. Methods: This article applies a theoretically grounded normative approach to explore the ethical conditions under which artificial intelligence can be integrated into clinical practice without compromising the moral foundations of medicine. The analysis is primarily based on Pellegrino and Thomasma’s concept of the internal morality of medicine and the clinician’s act of profession. It further draws on Kantian ethics of human dignity, Levinasian relational ethics, virtue ethics, and Vallor’s concept of technomoral wisdom. Results: AI systems do not satisfy the conditions under which moral responsibility can be ascribed to them. Clinical moral agency lies in the capacity to bear three distinct responsibilities—epistemic, relational, and phronetic—none of which can be fulfilled by AI. The implementation of AI in healthcare, therefore, must occur strictly under the condition of Meaningful Human Control, rather than as a technical function of human oversight over algorithmic outputs. To ensure that MHC can function as an effective and ethically grounded safeguard, we propose five normative requirements: primacy of clinical judgement, prohibition of forced automation, traceability and explainability, transparency towards patients, and retaining clinical authority. Dialogue between clinicians and patients should remain the foundation of clinical decision-making. The proposed normative requirements aim to preserve the internal morality of medicine in a form that harmoniously combines both technological progress and established medical ethics. Full article

This research proposal examines how the use of generative artificial intelligence (AI) in corporate social responsibility (CSR) communication shapes perceptions of authenticity and political–cultural polarization. Although AI is increasingly embedded in marketing and communication functions, CSR represents a uniquely sensitive domain in which sincerity, moral intent, and ethical deliberation are essential for effectiveness. Prior research suggests that AI-generated prosocial messages may influence perceived authenticity, particularly when social or ethical causes are politically charged. Building on this tension, the proposed research advances a three-study program to examine when and why AI-mediated CSR communication becomes polarizing and how such polarization shapes stakeholder perceptions. Study 1 develops a large-scale Instagram-based dataset of firm-generated CSR messages to identify message characteristics and issue domains associated with heightened stakeholder polarization. Study 2 evaluates whether generative AI can function as an ex ante diagnostic tool by forecasting polarization risk based solely on message content prior to publication. Study 3 experimentally compares AI-generated and human-generated CSR messages across low- and high-polarization causes to assess differences in perceived authenticity, trust, and anticipated stakeholder conflict. Full article

The rapid integration of generative artificial intelligence (GenAI) into education across all sectors has prompted a proliferating body of scholarship addressing the ethical, social, and environmental implications of these technologies. This narrative literature review synthesises international empirical, conceptual, and policy literature published between 2022 and 2026 to trace the evolving story of ethical concerns surrounding GenAI in education. Drawing on the moral philosophy of consequentialism, particularly the utilitarian ethics of John Stuart Mill, the review analyses six interconnected domains of ethical concern: environmental sustainability and the carbon footprint of AI infrastructure; algorithmic bias, ideological encoding, and the reproduction of misinformation; user dependency and the erosion of learner agency; the displacement of critical and creative thinking; data privacy and surveillance; and the orientation of major GenAI platforms toward profit-driven and capitalistic outcomes. Unlike systematic reviews that privilege methodological replicability, this narrative review foregrounds interpretive synthesis, tracing how the ethical discourse has shifted from early alarm and prohibition toward more nuanced frameworks for responsible integration. The review identifies a consequentialist tension at the heart of the debate: while GenAI offers measurable benefits in personalisation, accessibility, and efficiency, these gains must be weighed against distributed harms that disproportionately affect vulnerable populations, the natural environment, and the epistemic foundations of education itself. The review concludes with a set of guidelines for the ethical use of GenAI in educational contexts, grounded in the literature synthesised in the article. Full article

This entry focuses on an Aristotelian approach to contemporary discourses about the implications of Artificial Intelligence (AI) regarding what it teaches and learns, with special regard to virtue or arete, practical wisdom or phronesis, and human flourishing or eudaimonia. Even though AI technologies provide new options for personalized learning, adaptive assessment, and data-driven instruction, their increasing entrenchment in the education ecosystem raises fundamental philosophical questions about the essence of teaching and learning, and about how we become better people. Aristotle’s distinction between intellectual and moral virtues can help us determine whether AI meaningfully contributes to the cultivation of good judgment, ethical character, and responsible agency. While AI is not completely antithetical to virtue formation, its knowledge and skill acquisition cannot replace the social, experiential, and habituated processes through which virtues are grown. AI should be designed and deployed as a “technological partner” to support (not replace) the teacher’s moral and pedagogical role. Guided by Aristotle’s view of eudaimonia and the common good, this analysis suggests that education should be structured to promote human flourishing in the age of AI, ensuring that learners develop their capacities for ethical reasoning, autonomy, and co-responsible participation to build a more sustainable and just society. Full article

The rapid integration of artificial intelligence in higher education positively affects student satisfaction, engagement, and learning outcomes. However, students frequently report ethical unease, guilt, and concerns about dependency. The current literature offers a limited explanation for their coexistence, as both have been treated as parallel or independent outcomes. Hence, this review extends and integrates existing theories by reconceptualising cognitive and moral dissonance as a central psychological process that explains how student satisfaction with AI-mediated learning is produced, negotiated, and sustained. Following PRISMA 2020 guidelines, we adopted a two-layer explanatory review design, synthesising 40 Scopus-indexed studies (Layer 1 = 15 studies; Layer 2 = 25 studies) from 2016 to 2025. Layer 1 studies explicitly define dissonance-related explanatory mechanisms that influence satisfaction and continued AI use across contexts such as dissertation writing, programming education, and problem-based learning. Layer 2 encompasses satisfaction-based studies that report ethical or affective concerns in parallel without theorising their interaction. The findings suggest a recurring satisfaction–dissonance paradox, in which students often experience genuine or conditional satisfaction from performance gains while simultaneously managing their psychological discomfort through one or more regulation mechanisms. Further, persistent and escalated dissonance leads to withdrawal or full or partial adaptive behaviour. We propose these dynamics as a testable Dual-Process Satisfaction–Dissonance Framework (DPSDF), which includes five dissonance triggers, five regulation strategies, three feedback loops, and four behavioural outcomes. Further, five domain experts’ suggestions have been taken to provide specific practical implications. This framework extends understanding of AI-mediated learning and provides foundations for future theory and policy development in higher education. Full article

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

This article explores how Buddhist theories of mind-nature can inform ethical design in artificial intelligence (AI), focusing on AI as a supportive condition for human awakening and self-realization. Drawing on the doctrine of no-self, it argues that AI should not be treated as an autonomous moral subject, but as a contingent mirror of human data, design, and intention. Although present AI does not possess prajñā, it can serve as a mindfulness aid by making patterns of thought, emotion, and desire more visible. Building on the Five Precepts and Ten Wholesome Deeds, the paper proposes design and oversight principles oriented toward non-harm, truthful communication, fairness, and the reduction of greed, hatred, and delusion in digital environments. It concludes that AI ethics is inseparable from the human moral agency, and that cultivating a “digital Pure Land” depends on the moral choices of decision-makers, engineers, policy-makers, and users, thereby linking technical governance to spiritual practice. Full article

This article advances a constructive theological account of Human-Centered Artificial Intelligence (HCAI) for Faith-Based Organizations (FBOs) engaged in interreligious dialogue (IRD). Drawing on a practical–theological methodology, the study follows four interrelated steps—descriptive–empirical, interpretive, normative, and pragmatic—to examine how AI-enabled practices such as translation, textual analysis, and cross-scriptural synthesis are reshaping contemporary forms of dialogue among religious and non-religious communities. Through the empirical mapping of current AI applications, interdisciplinary interpretation informed by social and ethical analysis, and normative theological evaluation, the study identifies both the opportunities and risks of AI-mediated IRD. On this basis, it synthesizes three interdependent dimensions that structure the proposed framework: (1) Ethics, which clarifies the moral purpose and values guiding AI use; (2) Technology, which addresses mediation, governance, and power in AI systems; and (3) Humans, which centers institutional responsibility, agency, and sustainability within FBOs. From this synthesis, the article introduces an AI–IRD Integration Framework that translates theological and ethical reflection into practical guidance for responsible AI adoption. The study contributes an original interdisciplinary perspective that equips religious leaders, theologians, policymakers, and faith communities to engage AI not merely as a tool, but as a human-centered partner in fostering inclusive, sustainable, and ethically grounded dialogue in an era of AI–human coexistence. Full article

This study investigates how South Korean meditation teachers conceptualize core professional competencies in digitally delivered and AI-mediated contemplative contexts, addressing a gap in prior research that has emphasized effectiveness and technological scalability over teachers’ own understandings of authority and professionalism. Using Q methodology, the study identified shared subjective meaning structures among 21 certified meditation teachers in South Korea. From 133 competency-related statements derived from academic literature and practitioner sources, a 33-item Q sample was developed and analyzed through by-person factor analysis. The analysis revealed four distinct perception types of meditation teaching competencies: 1. Embodied Practice-Grounded, prioritizing the depth of personal meditative practice; 2. Relational Presence-Grounded, emphasizing intersubjective attunement between teacher and practitioner; 3. Pedagogical Judgment-Grounded, focusing on the strategic integration of theory and coaching practice; and 4. Ethical Self-Reflection-Grounded, centering on ongoing moral reflexivity and inner examination. The findings indicate that, in the face of AI-driven automation, meditation teaching competence is perceived not as a set of technical skills or digital literacy, but as a “way of being” rooted in the triadic integration of ethical self-awareness, relational presence, and embodied practice. Furthermore, the study suggests that in AI-mediated contemplative environments, professional competence in AI-mediated contemplative environments is defined less by technological adoption than by ethical discernment and responsibility for non-delegable aspects of guidance, advancing a practitioner-centered account of spiritual authority in the era of artificial intelligence. Full article

This study advances understanding of public trust in artificial intelligence (AI) by distinguishing between overall trust in AI as a system and trust in specific AI components, and by disentangling the roles of perceived capacity, risk, and personhood. Drawing on nationally representative survey data from 1099 U.S. adults collected in 2023 (AIMS dataset), the study estimates multiple regression models to examine how these evaluations shape trust across technical, organizational, and institutional dimensions. The results show that perceived cognitive capacity is the strongest positive predictor of both overall and component-level trust, while emotional and autonomous capacity primarily enhances trust in specific system components. Perceived social risk consistently undermines trust across all levels, whereas perceived personal risk mainly erodes trust in technical components. Importantly, support for granting AI legal or institutional status significantly increases trust, while moral consideration of AI exhibits limited direct effects, highlighting a critical distinction between institutional accountability and ethical concern. Together, these findings demonstrate that public trust in AI is not a unitary attitude but reflects multidimensional judgments about capability, risk, and governance. The study underscores the importance of institutional accountability and risk mitigation—alongside transparent communication about AI capabilities—for fostering sustainable public trust in AI. Full article

This study explores the potential synergy between Islamic finance and financial technology (fintech). This synergy may prove to be a strong civilizational tool to help in the propagation of the Islamic finance principles as long as it is done right. Such values are used to enhance social justice, fair allocation of wealth, and moral economic involvement in contrast to profit maximization, which is the ultimate aim of traditional finance. With the advent of fintech, new technologies like blockchain, artificial intelligence, and mobile platforms emerged. These innovations do provide a chance to bring the ideas of Islamic finance to a large scale. It is on the basis of the significant scholarly and business reports that this paper will comment on how fintech can contribute to better Shari’ah-compliant products, financial inclusion, and better, transparent, and more resilient economic systems. This paper identifies the opportunity in innovation and the challenges that exist in the Islamic finance industry. The main challenges are regulatory barriers, ethics, the absence of standardization/harmonization, and skilled workers. By the concerted effort of all stakeholders, we would be in a position to develop a collaborative ecosystem that would harness technology for the betterment of humankind. Finally, digitalized finance through fintech may contribute to sustainable civilizational development if designed, governed, and implemented according to maqasid al-Shari’ah principles and integrated within appropriate regulatory frameworks. Full article

The increasing integration of artificial intelligence (AI) in decision-making processes has amplified discussions surrounding algorithmic authority—the perceived epistemic legitimacy of AI systems over human judgment. This study investigates how individuals attribute epistemic authority to AI, focusing on psychological, contextual, and sociotechnical factors. Existing research highlights the importance of trust in automation, perceived performance, and moral frameworks in shaping such attributions. Unlike prior conceptual or philosophical accounts of algorithmic authority, our study adopts a relational and empirically grounded perspective by operationalizing algority through psychometric measures and contextual assessments. To address knowledge gaps in the micro-level dynamics of this phenomenon, we conducted an empirical study using psychometric tools and scenario-based assessments. Here, we report key findings from a survey of 610 participants, revealing significant correlations between trust in automation (TiA), perceptions of automated performance (PAS), and the propensity to defer to AI, particularly in high-stakes scenarios like criminal justice and job-matching. Trust in automation emerged as a primary factor, while moral attitudes moderated deference in ethically sensitive contexts. Our findings highlight the practical relevance of transparency and explainability for supporting critical engagement with AI outputs and for informing the design of contextually appropriate decision support. This study contributes to understanding algorithmic authority as a multidimensional construct, offering empirically grounded insights for designing AI systems that are trustworthy and context-sensitive. Full article

This study critically examines how Artificial Intelligence for Social Responsibility (AI for SR) is enacted within Thai education, using this Global South context to expose the universal dynamics of educational marketization. Drawing on Freire’s critical pedagogy and Habermas’s theory of lifeworld, the research employs a qualitative design grounded in critical phenomenology. Analysis of interviews, observations, and policy documents reveals that AI for SR is driven less by ethical participation than by policy compliance, funding agendas, and portfolio-driven competition. This dynamic transform responsibility from a moral practice into symbolic capital. Students become producers of symbolic output, and educators act as image managers for institutional displays. The study concludes by proposing a critical pedagogical framework that reclaims AI for SR as a public good, emphasizing dialog and social justice to resist this commodification. Full article

The emergence of self-driving vehicles raises not only technological challenges, but also profound moral and legal challenges, especially when the decisions made by these vehicles can affect human lives. The aim of this study is to examine the moral and legal dimensions of algorithmic decision-making and their codifiability, approaching the issue from the perspective of the classic trolley dilemma and the principle of double effect. Using a normative-analytical method, it explores the moral models behind decision-making algorithms, the possibilities and limitations of legal regulation, and the technological and ethical dilemmas of artificial intelligence development. One of the main theses of the study is that in the case of self-driving cars, the programming of moral decisions is not merely a theoretical problem, but also a question requiring legal and social legitimacy. The analysis concludes that, given the nature of this borderline area between law and ethics, it is not always possible to avoid such dilemmas, and therefore it is necessary to develop a public, collective, principle-based normative framework that establishes the social acceptability of algorithmic decision-making. Full article

This entry delineates artificial intelligence (AI) ethics and the field’s core ethical challenges, surveys the principal normative frameworks in the literature, and offers a historical analysis that traces and explains the shift from ethical monism to ethical pluralism. In particular, it (i) situates the field within the trajectory of AI’s technical development, (ii) organizes the field’s rationale around challenges regarding alignment, opacity, human oversight, bias and noise, accountability, and questions of agency and patiency, and (iii) compares leading theoretical approaches to address these challenges. We show that AI’s development has brought escalating ethical challenges along with a maturation of frameworks proposed to address them. We map an arc from early monisms (e.g., deontology, consequentialism) to a variety of pluralist ethical frameworks (e.g., pluralistic deontology, augmented utilitarianism, moral foundation theory, and the agent-deed-consequence model) alongside pluralist governance regimes (e.g., principles from the Institute of Electrical and Electronics Engineers (IEEE), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the Asilomar AI principles). We find that pluralism is both normatively and operationally compelling: it mirrors the multidimensional problem space of AI ethics, guards against failures (e.g., reward hacking, emergency exceptions), supports legitimacy across diverse sociotechnical contexts, and coheres with extant principles of AI engineering and governance. Although pluralist models vary in structure and exhibit distinct limitations, when applied with due methodological care, each can furnish a valuable foundation for AI ethics. Full article