Transdisciplinary skills and AI ethics: toward a techné-based lifeworld extension

The implications of Artificial Intelligence (AI) development and deployment have become ethically significant in our modern lives. While the majority of contemporary AI ethics frameworks are based on secular and Western norms, there is an increasing number of works related to religion-culture in the frameworks. The focus of this bibliometric study is on the global research that surrounds religion and culture as the foundation for ethical AI frameworks, with an emphasis on Islamic ethical principles. By using the Scopus database, a total of 63 publications from 2015 to 2025 were analyzed through Biblioshiny and VOSviewer. The analysis includes trends in publication output, leading authors, collaborative networks, etc. Results indicate an increase in scholarly output since 2022, with Southeast Asia, notably Malaysia and Indonesia, and the Middle East as the key contributors. Core themes include ethics, religion, language model, and ethical design, while religious values, such as Islamic ethics and maqasid al-shariah, are increasingly integrated into AI ethics discourse. Keyword co-occurrence and thematic mapping reveal three dominant clusters: (1) core ethical AI principles, (2) ethics technology, and (3) religion-informed value systems. While terms like “artificial intelligence,” “ethics,” and “language models” remain central, faith-based constructs are transitioning from peripheral to foundational in shaping future AI frameworks. This study demonstrates the growing intellectual momentum behind culturally and spiritually responsive AI ethics also the potential of Islamic epistemology to enrich global AI governance.

Historically, technology was always a key differentiator and an enabler for business orsocietal developments. With the rapid advancements in technology, businesses have beenable to increase their efficiency, productivity, or profitability through automation anddigitization. The European Union elaborated clear strategies that aims overall economicdevelopment trough digital transformation, one of the core technology components beingArtificial Intelligence (AI). While the added value of AI technologies, in terms ofoptimization, efficiency, automation is clear and undeniable, there are differentchallenges related to the ethical aspect of AI use. Hence the AI ethics discussions arecurrently very present in the public space. A collection of different AI ethical frameworkswas redacted either by the researchers or by governments or by the tech industry, butdeveloping consistent AI ethical system is still a grey area. Some of the main challengesrevealed by different researchers are related to the practical implementation of ethicalprinciples in AI technologies during its lifecycle.This paper proposes a conceptual framework for operationalizing AI ethical principles inbusiness contexts. The framework is based on a comprehensive literature review ofexisting AI ethical frameworks, which highlights the current gaps in their implementation.The proposed framework addresses these gaps by providing a step-by-step approach thatcan be easily integrated into existing business processes. It covers various stages of AIdevelopment, including problem formulation, data collection and preprocessing, modeltraining, model evaluation, and deployment. The practical validation of the frameworkwill be conducted in future work. The results suggest that the proposed framework canprovide a systematic approach to operationalizing AI ethical principles in businesscontexts, thereby contributing to the development of responsible AI systems.

This study presents a conceptual technical framework aimed at promoting ethical AI deployment within the procurement domain, with a particular focus on legal oversight. As the integration of artificial intelligence (AI) technologies in procurement processes becomes increasingly prevalent, concerns surrounding ethical considerations and legal compliance have come to the forefront. The framework outlined in this study offers a structured approach to addressing these challenges, emphasizing the importance of legal oversight in ensuring ethical AI practices. Drawing on existing literature and best practices, the framework outlines key components and principles for guiding the development, implementation, and monitoring of AI systems in procurement contexts. Central to the framework is the recognition of legal requirements and regulatory frameworks governing AI deployment, including data protection laws, liability provisions, and procurement regulations. By incorporating these legal considerations into the design and operation of AI systems, organizations can mitigate risks and ensure compliance with applicable laws. Additionally, the framework emphasizes the need for transparency and accountability in AI procurement processes, advocating for clear documentation, audit trails, and stakeholder engagement mechanisms. Furthermore, the framework outlines strategies for ethical AI design, including the identification and mitigation of algorithmic bias, the promotion of fairness and equity, and the protection of privacy rights. By embedding ethical principles into the development lifecycle of AI systems, organizations can foster trust and confidence among stakeholders while minimizing the potential for harm or discrimination. Overall, the conceptual technical framework presented in this study provides a comprehensive approach to promoting ethical AI in procurement, with a specific emphasis on legal oversight. By integrating legal requirements, ethical principles, and technical considerations, organizations can ensure that AI deployment in procurement processes is conducted responsibly, transparently, and in accordance with legal and ethical standards.

As AI technologies become more prevalent, it is crucial for students to develop responsible, ethical, and proactive AI engagement skills. Recent educational initiatives have focused on enhancing CS and engineering students' AI ethics education but have largely overlooked integrating these concepts across other disciplines. This paper presents and assesses a pioneering initiative that integrates AI ethics into university curricula through a collaborative framework between CS and domain educators. We introduced 1-3 week AI ethics modules in seven diverse courses from Art to Chemistry, incorporating case studies and hands-on activities using chat- or image-based Large Language Models (LLMs). Student surveys indicated significant gains in confidence regarding AI ethics discussions, application of principles, and reasoning skills. Our approach advocates for utilizing structured frameworks and faculty collaboration in embedding AI ethics into university curricula, enhancing students' practical skills and ethical understanding across diverse professional settings.

Artificial intelligence (AI) is reshaping higher education and career services, raising critical questions for hospitality students, educators, and industry. This paper examines the role and ethics of AI in hospitality career development. The literature identifies three key areas: the transition from traditional relational career counseling models to AI-enabled efficiency; the use of AI as a pedagogical tool; and ethical challenges concerning authenticity, bias, transparency, privacy, and overreliance. While AI enhances scalability, personalization, and access, it also risks homogenizing applications and undermining fairness in recruitment. The discussion argues for hybrid models in which AI delivers efficiency and data-driven insights while human career educators safeguard reflective practice, authenticity, and ethical judgment. Practical implications are outlined for universities to embed ethical AI literacy, for students to balance technological fluency with interpersonal competence, and for employers to complement AI screening with human oversight. Future research should examine longitudinal career outcomes, cultural fairness in recruitment algorithms, and governance frameworks for the responsible adoption of these algorithms. The paper concludes that AI should act as a complement rather than a replacement, ensuring that hospitality career services remain aligned with the sector’s people-centered ethos.

Purpose Artificial intelligence (AI) is currently influencing many aspects of business and the lives of humans. Understanding how AI may be built to operate ethically and satisfy the expectations of stakeholders is critical. Ethical ramifications of AI were investigated by several pioneering academics in the domains of economics, law, ethics and philosophy. They also created a set of guiding principles and criteria for future research. Despite this, little study has been done on the connection between AI ethics and employee commitment (EC) and competitive advantage (CA). Through mediation of the impact of responsible innovation (RI) and employee motivation (EM), this study aimed to explore the relationship between AI ethics and EC and CA. Design/methodology/approach Survey data from 206 respondents were evaluated using Process macro version 3.4 in SPSS 23 and AMOS 21.0. The study findings showed that the ethics of AI are important for boosting EC and the organization's ability to remain competitive, and a significant connection between them was discovered. Additionally, both RI and EM served as mediators. Parallel mediation has been tested. Findings AI ethics predicts RI and EM, which in turn drives EC and organization's CA. Furthermore, statistical significance was shown for both direct and indirect impacts. Practical implications Based on the study results, theoretical and practical consequences are examined. This study is the first to explore the interaction between AI ethics, EM, EC, CA and RI. Originality/value This piece of work is novel and one of a kind. The conceptual framework has not been studied earlier. The interplay of variables such as ethics of AI, CA, EC, RI and EM is novel.

Given the lack of national and institutional Artificial Intelligence (AI) governance frameworks, the governance and education of AI in Philippine higher education often rest with faculty. This makes course-level integration a productive starting point. This pilot study examined how graduate students develop ethical preparedness for AI, arguing that the relationship between such preparedness and their exposure to AI ethics instruction is mediated through operational literacy on AI. A mixed-methods intervention was conducted with 39 graduate students enrolled in the Master of Arts Major in Educational Management, who participated in a 20-hour AI literacy and ethics course embedded in a Graduate Seminar. To capture the Philippine education realities often overlooked in global frameworks, a researcher-developed questionnaire was employed to reconceptualize and measure AI literacy and ethical preparedness in the local context. Quantitative results showed significant improvements, with Artificial Intelligence literacy mediating the relationship between ethical preparedness and artificial intelligence literacy (β = .46, p < .001). Qualitative insights through classroom observations illustrated how students translated abstract principles into situated judgment. This paper extends AI ethics discourse by reframing it not only as a matter of principles but also as a function of operational literacies that enable educators to exercise situated ethical judgment, especially within local contexts.

Purpose: This study critically examines the achievements and limitations of the discourse on AI ethics to date. It highlights the need for a renewed perspective on the directions and challenges of AI ethics education, specifically through the lens of educational ethics. Methods: Through an analysis of prior research and a comprehensive literature review, this study examines the validity of the current AI ethics discourse, which primarily revolves around the development of ethical guidelines and principles. Furthermore, it explores the practical direction of AI ethics education from the perspective of educational ethics. Results: AI ethics discourse that lacks educational attention is vulnerable to side effects such as ethics washing and ethics bashing. AI ethics education through moral education as a subject should not merely consume the existing research outcomes of AI ethics discourse—such as ethical guidelines, checklists, and principles—but should also explore the potential for restoring poetic dwelling, and for revitalizing conversation through ethics by design. Conclusion: From the perspective of educational ethics, the direction and challenges of AI ethics education through moral education as a subject are as follows: first, to ensure that technical practices are informed by poetic thinking; and second, to guarantee focal moments of conversational engagement even within technical practices.

Transdisciplinary engineering curricula prepare future engineers with a holistic understanding of complex real-world problems, and the ability to tackle these problems with knowledge and skills in both engineering and non-engineering areas. What are transdisciplinary skills in the engineering education context? What learning activities can we design and implement to develop students’ transdisciplinary skills in the first-year engineering program? How can we assess transdisciplinary skills and evaluate the instructional effectiveness of these learning activities?The current study is an initial attempt to explore these questions. We introduce a conceptual framework ofusing systems thinking, empathy and metacognition asproxy indicators of transdisciplinary skills, and presentthe learning activities we have designed to developstudent competencies in these areas. In addition, wepropose an evaluation approach that includes a surveyinstrument and formative learning assessment, with which we investigate the relationships among empathy, systems thinking, and metacognitive skills in the context ofengineering education.

The increasing deployment of artificial intelligence (AI) in e-commerce has enabled the emergence of delegated commerce, wherein consumers authorize AI agents to autonomously execute purchase and payment decisions. While such systems promise convenience, efficiency, and personalization, their widespread adoption remains constrained by unresolved issues of consumer trust, particularly in financially consequential contexts. Prior research has largely examined general AI acceptance or human-AI interaction, offering limited insight into the specific conditions under which consumers are willing to delegate economic decision authority to AI agents. This paper proposes a multidimensional conceptual framework for understanding consumer trust in delegated commerce. Drawing on an interdisciplinary synthesis of trust theory, consumer behavior, AI ethics, and regulatory scholarship, the framework articulates trust as a conditional and context-dependent construct shaped by perceived risk, transparency, control mechanisms, system infrastructure, personalization, and liability arrangements. To ground and refine the framework, the study incorporates illustrative insights from expert interviews spanning AI development, e-commerce, psychology, ethics, and user experience. Rather than empirically validating trust determinants, the study aims to clarify core constructs, delineate boundary conditions across transaction risk and delegation levels, and surface key governance challenges specific to AI-mediated purchase and payment decisions. The proposed framework offers a structured foundation for future empirical research and provides practical guidance for designers and policymakers seeking to support responsible, trustworthy deployment of autonomous commerce systems.

With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as the use of video conferencing and metaverse platforms has increased due to COVID-19, ethics concepts and boundaries related to information and communications technology, cyber etiquette, AI ethics, and robot ethics have become more ambiguous. Because the definitions of ethics domains may be confusing due to the various types of computing platforms available, this paper attempts to classify these ethics domains according to three main platforms: computing devices, intermediary platforms, and physical computing devices. This classification provides a conceptual ethics framework that encompasses computer ethics, information ethics, cyber ethics, robot ethics, and AI ethics. Several examples are provided to clarify the boundaries between the various ethics and platforms. The results of this study can be the educational basis for the sustainability of society on ethical issues according to the development of technology.

Ship is divided into four stages depending on whether the crew is on board and whether remote controllers are controlled, and fully Autonomous Ships will make optimal decisions based on algorithms designed by designers without human intervention. However, due to the characteristics of Artificial Intelligence such as autonomy and unforeseeability, the decision of a fully autonomous ship cannot always be expected to be ethical. However, Artificial Intelligence should make decisions that reflect human values. Especially in situations where the risk of conflict is unavoidable so called accidental algorithm, it should be able to explain what principles and criteria Artificial Intelligence makes decisions and these decisions should be reasonably accepted by society. This discussion of the ethical issues of Artificial Intelligence is the most active in the field of self-driving cars. However, there is a limitation that human values are difficult to have consistency and universality. In particular, in ethical dilemma situations, "Dualism of Practical Reason" makes decisions different from rational and ethical judgments, and standards for ethical behavior are also recognized differently depending on regional, cultural, and economic situations. As a result, while recognizing the need for ethical guidelines for Artificial Intelligence, it is skeptical about establishing specific standards. Because the ethical dilemma situation of self-driving cars cannot be predicted and fully responded to the real problems that will arise in the process of use. However, coming up with countermeasures after the problem occurs is only a measure to remedy the damage that has occurred in reality and is not a way to solve the fundamental problem. In particular, Maritime Autonomous ships operating in waters all around the world must make decisions that are reasonably accepted by world society. Therefore, Ethical guidelines, including accidental algorithms, should be prepared by the International Maritime Organization and reflected their legal system in individual countries. In the entire process from the development stage to the use stage, program developers and stake-holders will have to comply with Ethical standards and respond to Maritime Autonomous Surface Ships’s ethical issue through complementary work based on accumulated information.

ABSTRACTAs Artificial Intelligence (AI) advancements create new ethical issues and challenge existing legal regulations, traditional ethical principles and data ethics education often fail to reflect these nuances. This study explores how collaborative game design among students with diverse backgrounds fosters transformative learning in data ethics within a college course. It examines a conversation about motivations for enrolling in the course, pre‐course reflections, and post‐course reflections from 32 participants in a semester‐long credit‐based class. Thematic analysis reveals parallels with Mezirow's transformative learning theory (1978) in four key phases of transformative learning. The preliminary findings demonstrate that students not only gained data ethics literacy but also internalized ethical thinking as part of their future goals. This poster highlights the potential of interdisciplinary and collaborative design‐based pedagogy to cultivate digital ethical self‐awareness and ethical judgements when navigating ethical issues in AI or machine learning (ML).

Alongside the revolutionary benefits of AI, it can cause numerous problems across the system development process. AI ecosytem players have recently started to interrogate the ethical biases implicit in AI-enabled applications and agents. The contestable nature of ethics and the complexity of AI-enabled applications has led to incoherent literature around AI ethical biases. The numerous conceptions of AI ethics and a multiplicity of ethical biases has compounded matters for researchers, practitioners, and policy makers. The current study proposes a conceptual framework to organize AI ethical biases. A narrative literature review was conducted to identify and group the biases into data biases, method biases and implementation biases. The CRISP-DM framework was used to classify the ethical biases. The emerging conceptual framework has four clusters that represents: System development phases, scope of ethical bias, exemplars, and possible solutions. The study extends the existing AI ethical frameworks and provides a unified communication artefact for practitioners.

AI ethics has become a common topic of discussion in both media and academic research. Companies are also increasingly interested in AI ethics, although there are still various challenges associated with bringing AI ethics into practice. Especially from a business point of view, AI ethics remains largely unexplored. The lack of established processes and practices for implementing AI ethics is an issue in this regard as well, as resource estimation is challenging if the process is fuzzy. In this paper, we begin tackling this issue by providing initial insights into the cost of AI ethics. Building on existing literature on software quality cost estimation, we draw parallels between the past state of quality in Software Engineering (SE) and the current state of AI ethics. Empirical examples are then utilized to showcase some elements of the cost of implementing AI ethics. While this paper provides an initial look into the cost of AI ethics and useful insights from comparisons to software quality, the practice of implementing AI ethics remains nascent, and, thus, a better empirical understanding of AI ethics is required going forward.