Search Results (2,284)

Algorithmic governance reshapes the exercise of authority by distributing decisions across code rather than concentrating them in identifiable human agents. This paper argues that three functions traditionally served by personified authority come under structural pressure: the anchoring of accountability, the adjudication of value conflicts, and the reproduction of the conditions for collective self-governance. The ethical stakes concern the capacity of affected persons to hold decision-makers to account, to participate in forming the norms that govern them, and to act as members of a self-governing polity. To make these dynamics visible, the paper develops three diagnostic concepts through blockchain governance and applies them to AI-based decision-making. Accountability anchor displacement names the condition in which distributed architectures dissolve or functionally bypass the subject to whom the demand for an account must be addressed. The Althusian dilemma describes polycentric normative orders that lack a recognized mechanism for adjudicating value conflicts. The erosion of civic virtue through exit captures how governance architectures that make exit cheaper than voice systematically weaken the institutional conditions for collective self-governance. The paper draws on the theory of Althusius, Hirschman, and Machiavelli, alongside contemporary scholarship on the EU Artificial Intelligence Act and the Council of Europe Framework Convention on AI. The core claim is that algorithms cannot cancel the need for institutional anchors that hold accountability in place, adjudicate value disputes, and sustain civic participation. Full article

The increasing integration of Artificial Intelligence (AI) into classroom practice has created the need for valid instruments to assess teachers’ readiness for AI integration. The present study developed and psychometrically validated the Teachers’ Readiness to integrate Artificial Intelligence (TRi-AI) scale, conceptualizing readiness as a multidimensional construct comprising Cognitive Conditions, Affective Conditions, Commitment, Self-efficacy, Ethical Considerations, and Worries. A total of 645 in-service primary and secondary school teachers participated in the study. The findings from Exploratory and Confirmatory Factor Analyses supported the hypothesized six-factor structure. The final scale comprised 36 items, χ²(579) = 1098.950, p < 0.001, CFI = 0.943, TLI = 0.938, RMSEA = 0.037, 90% CI [0.034, 0.041], and SRMR = 0.044. Reliability, convergent validity, discriminant validity, and evidence of practical measurement invariance across gender substantiated the psychometric adequacy of the instrument. Additionally, Exploratory Graph Analysis, Item Stability Analysis, and Network Comparison Test provided complementary psychometric evidence for the robustness of the proposed framework. The TRi-AI scale is proposed as a reliable and valid multidimensional instrument that can support both future research and educational practice. Full article

The rapid emergence of large language models (LLMs) is reshaping Computer Science (CS) education. Yet, little is known about how students engage with these tools as both technical and ethical learning partners in authentic project-based environments. This qualitative study investigates how 29 undergraduate CS students used LLMs while developing open-ended AI applications in a project-based course. Analysis of project documentation, reflection logs, and presentation transcripts revealed three interconnected forms of student–LLM interaction. First, we observed a technical partnership, in which students leveraged LLMs for code generation, debugging, architectural planning, and API integration while working on complex development challenges. Second, there was an ethical-reflective partnership, as students negotiated transparency, originality, bias, and the risks of over-reliance, demonstrating elements of critical AI literacy. Third, students reported perceived shifts in their learning practices, including increased confidence, more intentional problem-solving, and changes in how they sought support from peers and instructors, suggesting a reconfiguration of self-regulatory learning practices in interaction with LLMs. Together, these findings suggest that LLMs can be understood not merely as productivity tools but as multifaceted partners involved in the technical, ethical, and self-regulatory dimensions of learning in CS education. The study offers theoretical and practical implications for designing AI-enabled curricula that cultivate responsible, reflective, and critically engaged use of LLMs. Full article

Digital governance has increasingly emerged as a critical institutional mechanism for strengthening public administration, enhancing accountability, improving service delivery, and advancing inclusive development across Africa. Governments across the continent have expanded investments in e-governance systems, digital public infrastructure, artificial intelligence (AI)-enabled administrative systems, interoperable service platforms, and data-driven governance reforms as part of broader modernization and Sustainable Development Goal (SDG) agendas. Despite these developments, digital governance outcomes remain uneven and are frequently constrained by fragmented governance architectures, weak institutional coordination, administrative capacity deficits, regulatory limitations, and persistent socio-economic inequalities. To address these challenges, this study conducts a qualitative Systematic Literature Review (SLR) guided by PRISMA 2020 reporting standards to examine how digital technologies interact with governance systems, institutional structures, administrative capability, and socio-technical inequalities across African public sectors. The review synthesized 42 included studies published between 2015 and 2025 using structured Boolean search strategies, predefined inclusion and exclusion criteria, abductive thematic synthesis, and framework-oriented analytical procedures. The findings identify three interconnected governance constraints shaping digital transformation outcomes across African public sectors: fragmented and centralized governance arrangements; institutional and administrative capability deficits; and persistent digital inequalities and exclusionary governance systems. Although digital governance reforms demonstrate important potential for improving transparency, interoperability, citizen participation, financial inclusion, and administrative efficiency, sustainable transformation outcomes depend heavily on institutional coordination, adaptive governance systems, digital inclusion, regulatory effectiveness, and accountable AI governance arrangements. Building on the synthesized evidence, the study develops a decentralized AI-enabled digital public governance framework linking decentralization, interoperability, institutional coordination, digital inclusion, adaptive governance, and ethical AI governance to sustainable public-sector transformation outcomes. The study contributes theoretically by reconceptualizing digital governance as an institutionally embedded governance transformation process rather than merely a technological modernization agenda. The findings further contribute to sustainability debates by demonstrating how inclusive and decentralized digital governance systems can strengthen institutional resilience, public-sector innovation, and sustainable development outcomes across diverse African governance contexts. Full article

Artificial intelligence is a rapidly evolving technology whose capabilities are expanding quickly, leaving instructors across higher education to determine how to integrate it into teaching with little shared guidance. This paper reports on a Faculty Learning Community comprising seven faculty members from computer science, management information systems, systems engineering, economics, development studies, political science, and geography at a technological university in the northeastern United States. Through collaborative autoethnography, the group developed a convergence–divergence framework for AI-integrated pedagogy, visualized as a daisy: a shared core of AI literacy, ethical and risk awareness, and governance frameworks, surrounded by discipline-specific petals reflecting each field’s distinct conceptualization and application of AI. Seven disciplinary vignettes illustrate the framework in practice, revealing a common pattern across disciplines: AI tools offer possibilities and efficiencies while simultaneously obscuring risks and reproducing biases that require deliberate human attention. Drawing on ecological systems theory, the framework offers faculty a multi-level structure for designing AI-integrated courses that honor both shared foundations and disciplinary authenticity. Full article

Artificial intelligence (AI) has transformed digital marketing in so many ways because it enables intelligent, personalized, and data-driven customer experiences. Nonetheless, current research remains fragmented, mostly because it emphasizes the direct effect of AI on marketing performance while overlooking the instruments through which AI influences sustainable digital marketing systems (SDMSs) and the institutional conditions necessary for its proper usage. To fill in the gaps, this study develops and empirically tests an integrated Stimulus–Organism–Response (S–O–R) framework that conceptualizes AI-enabled sustainable digital marketing systems as the technological stimulus that influences sustainable digital marketing performance through customer satisfaction, customer engagement, and sustainable customer outcomes. The model incorporates sustainable customer outcomes and institutional sustainability capability as contextual constraints; it includes firm readiness and the regulatory environment as institutional sustainability capabilities that enable ethical and accountable AI integration. Using a quantitative, cross-sectional research design and survey data from 720 AI users, the model is analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that AI-enabled sustainable digital marketing systems can tremendously improve sustainable digital marketing performance both directly and indirectly through customer satisfaction, engagement, and loyalty. Strategic integration, institutional sustainability capabilities, and supportive regulatory environments further strengthen these relationships. The study strengthens the S–O–R framework by including technological, psychological, and institutional mechanisms into a unified model of sustainable digital marketing. It also accelerates responsible AI research by establishing how customer-centered AI deployment and institutional sustainability capabilities both promote long-term customer value creation and sustainable organizational performance. This provides practical guidance for developing ethical and sustainable AI-enabled marketing strategies. Full article

As artificial intelligence transforms the way educational systems interpret students, personalise teaching and automate pedagogical decisions, the persistent use of learning styles as a basis for personalisation demands renewed critical review. Although learning styles remain attractive to teachers because they offer an accessible language for recognising learner diversity, their empirical fragility becomes more problematic when transferred to adaptive platforms, recommender systems and data-based learning environments. This scoping review examines the evidence supporting learning-style models as factors for personalisation and analyses how recent literature reconfigures this debate in the context of AI-mediated education. The findings suggest that the most defensible contribution of learning styles does not lie in their classificatory capacity, but in the pedagogical concern they express, namely avoiding uniform teaching. However, AI does not solve the problem by classifying students better. Its educational value lies in supporting dynamic, revisable, and teacher-mediated interpretations based on observable, contextual, and ethically used evidence about learning. Full article

This study examined employer-perceived competency discrepancies, anticipated competency requirements, hiring barriers, and valued educational experiences in Utah. Survey data were collected between September and October 2025 from 226 individuals with hiring responsibility in Utah-based organizations. The Ranked Discrepancy Model (RDM) quantified discrepancies between respondents’ ratings of competency importance and their satisfaction with demonstrated employee proficiency. Respondents reported the largest discrepancies for time management, critical thinking, conflict resolution, ethical judgment, and verbal communication of ideas. Respondents rated communication, collaboration, analytical thinking, and lifelong learning highest among competencies anticipated to matter most over the next five to ten years. Most employers identified AI and machine learning as the most disruptive emerging technology. Our study provides timely evidence to inform organizational recruitment strategies, education administration, and workforce development policies. Full article

Generative Artificial Intelligence (GenAI) is reshaping technology-mediated learning environments in higher education, yet the structural heterogeneity of student adoption patterns—particularly across multimodal dimensions beyond text—remains empirically under-characterized. This study develops an empirically derived, data-driven taxonomy of GenAI adoption among university students, identifying distinct user profiles and their disciplinary and ethical risk implications. A quantitative, cross-sectional design was employed with a disciplinarily quota-balanced sample of 3415 students from eight Ecuadorian public universities, stratified across seven areas of knowledge according to the UNESCO classification. K-means cluster analysis on five continuous multimodal variables (text generation, mathematical problem-solving, programming, image generation, and music generation) yielded four distinct profiles: Passive (44.3%), Artist (24.4%), Technical (18.3%), and Comprehensive (13%). Profile membership showed a significant structural association with academic discipline (χ² = 517.85; Cramér’s V = 0.225). Profiles differed substantially in their self-reported propensity for intellectual delegation to AI systems, with the Comprehensive profile reporting the highest levels (η² = 0.114, 95% CI [0.092, 0.139]). These findings suggest that ethical risk in AI-mediated academic environments is not uniformly distributed but structurally associated with whether outputs are verifiable or directly presentable, with implications for differentiated AI literacy programs and institutional governance frameworks in higher education. Full article

Artificial intelligence (AI) has become a key driver of transformation in educational contexts, influencing both teaching and learning processes as well as the digital competencies required by teachers and students. In this context, understanding how AI is perceived, used, and valued is essential for guiding educational innovation in a critical and sustainable way. This study examines digital competence in artificial intelligence and its relationship with educational innovation, perceived threats, and job satisfaction, comparing university students and in-service teachers. The research adopts a quantitative approach based on a questionnaire administered to 355 participants. The results indicate that students demonstrate higher levels of technical skills, innovative vision, and more positive attitudes toward the educational use of AI, whereas teachers adopt a more cautious stance, mainly due to a lack of specific training and the absence of clear institutional guidelines. Significant differences are also found regarding perceived threats, with students showing greater awareness of risks such as plagiarism, algorithmic bias, and the potential erosion of critical thinking. In contrast, no significant differences are identified in ethics or job satisfaction. In conclusion, the study reveals a competence gap between both groups and highlights the need to redesign initial teacher education and promote continuous professional development in AI. Finally, it emphasizes the importance of integrating artificial intelligence into education from a critical, ethical, and human-centered perspective. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being used in medicine to improve the efficiency, accuracy, and timeliness of patient care. In prehospital tele-emergency medicine, AI also has the potential to address various challenges and support tele-emergency physicians. This study focuses on a specific AI-based decision support system being developed for use by practitioners. While technical feasibility is a prerequisite for the successful implementation of this system, social, ethical, and patient-centered considerations are equally important. Methods: The acceptance and perceptions of the German public, as potential patients, regarding the implementation of a specific AI system for prehospital tele-emergency medicine were assessed through an online survey. The results provide qualitative and quantitative insights into participants’ attitudes and acceptance. Results: The perceived advantages and disadvantages of implementing AI in prehospital tele-emergency medicine, as seen by potential patients, were identified, along with moderate acceptance of the technology. Participants emphasized the importance of different factors, including time efficiency, safety, AI recommendation accuracy, responsible system use, data and legal protection, technical stability, and positive impact on physicians’ work and patient outcomes. Conclusions: The findings enhance understanding of public perceptions regarding the use of AI in prehospital tele-emergency medicine and provide a valuable bioethical foundation for guiding the responsible integration of AI into tele-emergency care. Full article

Artificial intelligence (AI) is reshaping the hospitality industry at an unprecedented pace. However, existing hospitality research has overwhelmingly concentrated on customer-facing applications, including service robots, chatbots, personalization, and revenue management, while largely overlooking the internal operational systems that sustain hotel performance. As a result, the concept of AI maturity within back-of-house hotel operations remains theoretically undefined, fragmented across functional domains, and lacks an integrated framework for assessment. This study addresses this critical gap by asking a fundamental research question: What does AI maturity actually mean for hotel back-of-house operations? Drawing upon a systematic literature review following the PRISMA protocol, this study synthesizes evidence from 18 studies spanning the interdisciplinary fields of hospitality management, operations management, information systems, and artificial intelligence to examine how AI is transforming core internal hotel functions. The review identifies current applications, implementation patterns, organizational enablers, barriers to adoption, and emerging trends across human resource management, procurement, finance and accounting, inventory management, housekeeping planning, maintenance, energy management, and managerial decision support. Building on these findings, the study develops the Artificial Intelligence Maturity in Back-of-House Operations (AIM-BoH) Framework, a domain-specific conceptual framework designed to conceptualize AI maturity across hotel back-of-house functions. The framework conceptualizes AI maturity as a multidimensional organizational capability encompassing technological adoption, process automation, decision intelligence, data readiness, human–AI collaboration, governance and ethical preparedness, and measurable operational outcomes. By moving beyond technology-centric perspectives, the framework provides a comprehensive model for understanding how AI creates organizational value through the integration of internal hotel processes. The proposed framework advances hospitality literature by establishing a common theoretical foundation for understanding AI maturity in internal hotel operations while offering hotel executives a structured conceptual lens for considering organizational capability development in the planning of digital transformation initiatives. The article concludes by proposing a research agenda for the empirical validation, refinement, and cross-cultural application of the AIM-BoH Framework, positioning it as a reference model for future hospitality AI research and practice. Full article

Artificial Intelligence (AI) literacy has emerged as a critical 21st-century competence, yet its integration within interdisciplinary STEAM (Science, Technology, Engineering, Arts, and Mathematics) education remains fragmented. This study addresses this gap by investigating how AI literacy is conceptualized, implemented, and evaluated within STEAM learning environments. Following the PRISMA guidelines, a systematic literature review was conducted on empirical studies published between 2015 and 2025 to examine pedagogical approaches, methodological designs, AI technologies, and arts disciplines. An analysis of 32 studies revealed key insights into the development of AI literacy within STEAM education: (1) AI literacy is most often fostered through active and interdisciplinary learning rather than through lecture-based instruction alone, (2) AI serves both as a subject of study and as a tool for learners to create, design, investigate, and solve problems, (3) arts integration in AI-STEAM education typically support broader learning objectives and less frequently is assessed as distinct learning outcome, (4) ethical and societal aspects of AI literacy receive less attention than technical and computational skills, (5) the predominance of non-formal settings, short interventions, and context-specific studies indicates limited evidence on long-term progression, scalability, and sustained outcomes. The review contributes to the emerging field of AI-STEAM education by mapping empirical research from the past decade and developing two analytical tools: a five-cluster framework for learning objectives and a four-cluster framework for arts integration in AI-STEAM education. It concludes with a multi-level synthesis that identifies key educational and technological implications for the design, implementation, and future development of the field. Full article

Generative artificial intelligence (GenAI) offers opportunities for dialogic scaffolding, scientific reasoning, formative feedback, and instructional design, while raising cognitive, epistemic, didactic, and ethical risks. This review synthesized 34 publications from 2023 to 2026 that were identified through searches of Scopus, Web of Science, and ERIC. Guided by didactic mediation, epistemic practices, and metacognitive regulation, the synthesis indicates that GenAI’s educational contributions depend on three dimensions: learner regulation, teacher mediation, and disciplinary grounding. Six interrelated conditions are organized within these three dimensions to position GenAI as a mediating artefact rather than an epistemic authority. Given the heterogeneous and emerging evidence base, this framework constitutes an interpretive synthesis requiring empirical validation across science education contexts. Full article

The integration of artificial intelligence (AI) into pre-university education has emerged as a priority area for educational research and development, prompting renewed attention to teacher preparation, curriculum design, and digital literacy. Motivated by Sustainable Development Goal 4 (SDG 4) and the principles of Education for Sustainable Development (ESD), this study presents a systematic literature review (SLR) examining research trends in AI education within pre-university educational settings. Following PRISMA guidelines, the review analysed 42 studies published spanning the period from 2009 to May 2026, although most were published from 2019 onwards. The findings identify four major thematic areas: teacher education, curriculum development and pedagogical foundations, AI literacy and ethical competencies, and the educational implications of generative AI. The evidence indicates that effective AI integration depends on teacher preparedness, structured curricular frameworks, and critical and ethical approaches that promote responsible digital citizenship and inclusive educational practices. The review also highlights the need to strengthen institutional policies, regulatory frameworks, and professional development. Research gaps include the scarcity of longitudinal studies, limited classroom-based empirical evidence, and the lack of standardised instruments for assessing AI literacy. Overall, the findings may inform inclusive, interdisciplinary, and pedagogically grounded educational models that enable the critical and responsible integration of AI, aligned with the development of equitable, resilient, and sustainable educational systems. Full article