Search Results (355)

With the development of the digital intelligence era, generative AI is being widely used in scientific research, and its impact on graduate students’ research competence has attracted much attention from the academic community. Based on cognitive distribution theory and self-efficacy theory, this study classifies AI applications into three levels from basic to advanced—technical support AI use, text development AI use, and transformation AI use—explores their effects on graduate students’ research competence, and examines the mediating effect of critical thinking and the moderating effect of research self-efficacy. The results of the empirical analysis show that all three types of AI use behaviors are significantly correlated with research competence, with the strongest correlation for text development type and the weakest for technical support type. In the relationship between the three types of AI use behaviors and research competence, critical thinking plays a significant positive mediating role, and research self-efficacy plays a significant moderating role. Universities and tutors should guide students to focus on higher-order AI use behaviors in the text development and transformation categories, promoting the use of critical thinking to avoid technology misuse and improving research self-efficacy to help students accumulate confidence and support their research. Full article

Engineering education is increasingly shaped by two converging developments: accelerating sustainability transitions and rapid advances in artificial intelligence (AI). However, in many application-oriented undergraduate programs, sustainability learning remains fragmented, methodologically limited, and weakly connected to authentic engineering decision-making. To address this gap, this study proposes AI-SEE (Artificial Intelligence-Integrated Sustainable Engineering Education), a pedagogical framework that integrates AI across the curriculum as both a cognitive scaffold and a resource for system-level analysis. Emphasizing human–AI collaboration, AI-SEE is designed to be feasible and scalable within application-oriented higher education contexts. The framework comprises four interrelated pillars: intelligence-driven, green-empowered, responsibility-leading, and practice-integrated. Drawing on an empirical case from transportation-related programs at Nantong University, the study employs a qualitative comparative design and conducts semi-structured interviews with 144 undergraduates at the end of their eighth semester (control group n = 70; pilot group n = 74). Interview data were analyzed using thematic analysis informed by constructivist grounded theory and the Gioia coding approach. The findings suggest that participation in AI-SEE is associated with differentiated patterns of sustainability consciousness. At the knowledge level, students reported more systematic and interdisciplinary understandings that extended beyond environmentally reductionist perspectives to include life-cycle thinking, social equity, and long-term considerations. At the attitudinal level, students described enhanced ethical reflexivity and evolving professional self-concepts, shifting from a focus on technical execution toward broader value-oriented roles. At the behavioral level, students reported more extensive knowledge-to-action translation across personal, academic, and career-related domains. Overall, AI-SEE provides a transferable pedagogical pathway for integrating AI into engineering education to support the development of sustainability consciousness in higher education. Full article

The rapid spread of generative artificial intelligence (AI) in higher education creates both opportunities for innovation and challenges for academic integrity, ethical use, and students’ critical thinking, particularly in scientific writing. This study examines whether a synchronous live coaching format can support students in developing reflective and responsible AI practices. A mixed-methods cross-sectional evaluation was conducted at a German distance-learning university with a strong focus on health and social sciences. An online survey was administered to 168 students who participated in voluntary live coaching sessions on “AI in Scientific Writing”. Quantitative items assessed perceived competence gains, ethical awareness, and confidence in handling AI tools, while open-ended questions captured qualitative feedback on the format’s strengths and improvement needs. Students reported that the coaching enhanced their understanding of responsible AI use and scientific integrity and valued the opportunity for open discussion, peer interaction, and the supportive attitude of instructors. Reflective and dialogic elements were perceived as particularly beneficial. Overall, the findings suggest that synchronous live coaching can contribute to fostering ethical awareness and higher-order thinking in AI-supported academic work, especially when it integrates structured input with dialogue, reflection, and peer learning. Full article

Background: Large language models (LLMs) are increasingly proposed as clinical decision support tools. However, their reliability in the emergency department (ED) triage remains insufficiently validated. This study aimed to evaluate the performance and limitations of multiple LLMs in triage using a large retrospective dataset. Methods: We conducted a retrospective analysis of 39,375 anonymized patient cases from the ED of AHEPA University General Hospital, Thessaloniki, Greece (June 2024–July 2025), extracted from the hospital’s electronic medical record system. All cases were triaged in real time according to the Emergency Severity Index (ESI) by 25 emergency physicians. In cases of uncertainty, a senior emergency physician was consulted. Seven LLMs (ChatGPT-5 Thinking, ChatGPT-5 Instant, Gemini 2.5, Qwen 3, Grok 4.0, Deep Seek v3.1, and Claude Sonnet 4) were evaluated against the physician-assigned ESI level (reference standard). Outcomes included triage score agreement (quadratic weighted kappa, κw), clinic referral accuracy and admission prediction. Subgroup analyses were performed by referral clinic and admission outcome. The study was conducted in accordance with TRIPOD-AI reporting guidelines. Results: Model performance varied substantially. DeepSeek and Claude Sonnet 4 achieved the highest agreement with physician-assigned ESI (κw ≈ 0.467; raw accuracy: 61.7%). In contrast, GPT-5 Instant performed poorly across all evaluation metrics (κw = 0.176; 95% CI: 0.167–0.186). Claude Sonnet 4 demonstrated the best performance in clinic referral (67.1%; κ = 0.619) and admission prediction (κw ≈ 0.46). Subgroup analyses indicated higher performance in pediatric cases and organ-specific complaints, such as ophthalmology (up to 81% accuracy). LLMs also showed tendencies toward over- or under-triage. Conclusions: Current LLMs demonstrate promising but inconsistent capability in triage. While selected models achieved moderate alignment with physician ESI decisions, none achieved strong agreement (κ > 0.80). LLMs are most suitable as supervised decision support tools, particularly in anatomically well-defined clinical scenarios, rather than as autonomous systems. Full article

AI-enabled chatbots are increasingly positioned as a remedy for breastfeeding support gaps in rural maternal health, offering private, immediate assistance amid persistent shortages of lactation specialists and limited access to care. However, their clinical promise remains constrained by the probabilistic nature of large language models, which can generate hallucinations that undermine maternal–infant safety. This article argues that safely integrating AI into breastfeeding support requires treating hallucination not as a singular technical flaw but as a systems-level risk shaped by design, governance, and use context. We identified key risks of AI systems that could result in hallucination such as, false citations, transcription errors, prompt injection and jailbreaking, and incorrect generalization or personalization, and analyze how each error introduces distinct safety vulnerabilities. Drawing from systems thinking, we outline mitigation strategies including retrieval-augmented generation grounded in authoritative breastfeeding sources, layered guardrails, adversarial testing, uncertainty-aware messaging, and domain-specific fine-tuning. By linking AI system design choices to downstream health consequences in resource-constrained settings, this paper reframes AI-assisted breastfeeding support as a governance challenge central to equitable, safe maternal health innovation. Full article

Critical thinking is emphasized across ACPE Standards 2025, the Pharmacist Patient Care Process, interprofessional education (IPE) frameworks, and licensure preparation (NAPLEX). Despite this, pharmacy education lacks a practical, theory-grounded framework that operationalizes critical thinking as an observable, assessable reasoning process, particularly in team-based and interprofessional contexts. We developed the AIM (Analyze–Interpret–Manage) framework by integrating the Delphi Consensus definition of critical thinking with the AAC&U VALUE framework, translating foundational theory into a concise, measurable, stage-based model applicable to both individual and collective cognition. AIM was tested using qualitative analysis of transcripts of student team discursive narratives of an assigned IPE scenario. Reasoning behaviors were coded by AIM stage and mapped to the 2016 IPEC Core Competencies and the 2025 NAPLEX competencies to ensure professional relevance and external validity. AIM reliably distinguished discrete stages of critical thinking across teams, revealing consistent patterns in how learners analyzed information, interpreted clinical and ethical significance, and managed decisions collaboratively. Mapping demonstrated strong alignment between AIM stages and IPEC and NAPLEX competencies. Our novel AIM framework offers a scalable approach for defining, teaching, and assessing team-based critical thinking in pharmacy education. By operationalizing critical thinking as a staged reasoning process aligned with professional standards, AIM fills a critical gap between educational theory, interprofessional practice, and licensure preparation. Full article

Against global challenges like climate change and biodiversity loss, sustainable development is the core orientation of engineering education transformation. Cultivating talents with interdisciplinary perspectives, systemic thinking and AI literacy is crucial for implementing the UN 2030 Sustainable Development Agenda. However, AI education focuses on seniors or graduates, with freshmen’s use of AI acting as “cognitive partners” for knowledge construction and complex problem-solving understudied, constraining AI’s potential in fostering early systemic thinking. We present a novel teaching practice integrating generative AI into an “AI-Environmental System Analysis” module, with Sousa chinensis habitat conservation as the case. Using a design-based research paradigm, we evaluated 24 student groups via system analysis briefs, AI usage reflections and course assessment data. Results show that the module effectively guided students to establish preliminary system analysis frameworks, with over 70% of groups identifying complex interactions among environmental factors. Students’ AI applications ranged from information retrieval to scenario simulation, initially forming systemic thinking and responsible AI literacy for sustainable development. This study provides a replicable paradigm for integrating AI and sustainable development education, clarifies the key role of structured instructional scaffolding, and enriches sustainable development-oriented engineering education pathways. Full article

Emerging hospitality markets confront a two-speed ecosystem where operational digitalization outpaces strategic AI readiness, creating a benefit–feasibility gap. Providers recognize substantial technology value yet face implementation constraints from costs, integration complexity, and skills shortages, while guests demonstrate acceptance conditional on trust, with privacy concerns suppressing willingness to pay. Drawing on dual-perspective empirical evidence from Albania’s accommodation sector consisting of a national provider readiness assessment (N = 1821) and a guest acceptance study (N = 689) conducted in prior research, this Design Science Research study develops a segment-differentiated technological blueprint through systematic integration of Design Thinking, service blueprinting, and systems thinking methodologies. Integrated TAM-TOE-DOI framework analysis reveals three distinct provider segments requiring differentiated implementation pathways: Tech Leaders positioned for AI capabilities, Selective Adopters benefiting from smart modules, and Skeptics requiring foundational capabilities. Empirical evidence establishes that regional ecosystem characteristics outweigh organizational scale in determining adoption feasibility, trust operates as a gating condition moderating acceptance and financial commitment, and supply–demand misalignment creates bottlenecks invisible to single-perspective assessments. Theoretical contributions extend TAM-TOE-DOI frameworks from explanatory constructs to design requirements, conceptualize supply–demand alignment as an adoption mechanism, and generate two generalizable design principles: dual-constraint satisfaction requiring simultaneous provider feasibility and guest acceptance, and trust-as-architecture embedding trust mechanisms as structural properties. The proposed segment-differentiated technological blueprint offers actionable implementation pathways aligned with varying levels of provider readiness, providing transferable guidance for policymakers, technology vendors, education providers, and accommodation providers across the Western Balkans, the Mediterranean, and other post-transition economies facing similar heterogeneity in readiness and resource constraints. Full article

With the rise of social media and the sharing of information, as well as the use of AI tools like ChatGPT in education, the ability to evaluate information credibility has become a crucial skill. The CREDIBLE framework, standing for Credibility, Reliability, Evidence, Date, Intent, Bias, Logic, and Expertise, offers a practical, student-friendly approach to source evaluation, especially suited for secondary and postsecondary learners. Unlike models and frameworks designed for higher education, CREDIBLE helps learners critically assess both online and AI-generated content. This paper introduces the framework and explores how educators can embed it into instruction to foster critical thinking, academic integrity, and responsible digital literacy. Full article

A strategic approach to integrating generative AI (GAI) into design education is explored in this article to enhance students’ creativity, critical thinking, and practical skills. Based on a cross-departmental initiative at National United University, Taiwan, a multi-level curriculum is proposed, combining foundational to applied courses. A five-phase design process, problem definition, attribute framing, keyword extraction, AI generation, and refinement, was used to guide student learning tools, including ChatGPT (powered by GPT-4o), Stable Diffusion XL (SDXL) 1.0, and Leonardo.ai (Phoenix model), supporting rapid ideation and decision-making. Case studies in industrial and architectural design demonstrate practical applications. Ethical issues are reviewed. The results show increased engagement, idea diversity, and faster iteration in student design work. Full article

This essay describes a leadership domains and capabilities framework and a futures thinking framework that educational leaders can use to help them navigate successfully through turbulent times. The leadership framework includes seven stable domains, and 37 associated capabilities. For this essay, the focus is on the domains associated with setting directions and understanding contexts (sensemaking). The futures framework uses past, current, best and next practices, and prediction pathways, to consider near and distant futures. The use of these frameworks is illustrated through consideration of three pressing issues facing educational leaders across the world: improving student feedback, developing future-ready capabilities, and adopting artificial intelligence (AI). Educational leaders are encouraged to have a view of leadership and futures, so they are better able to set direction and act as sense makers for their organisations, and ultimately lead a more successful organisation. Full article

This study investigates the interplay between entrepreneurial leadership and innovation performance in Jordanian IT firms, with a specific focus on the strategic role of Artificial Intelligence (AI). Grounded in a quantitative methodology, data were collected via a structured questionnaire from 162 professionals within the Jordanian IT sector. The research model positions AI not merely as a tool but as a potential catalytic factor, examining its direct and moderating effects on the leadership–innovation dynamic. Entrepreneurial leadership was assessed through the dimensions of innovative thinking, pro-activeness, and risk-taking, while innovation performance was measured across product, process, and organizational domains. The findings demonstrate that entrepreneurial leadership exerts a significant positive influence on innovation performance. Beyond the primary leadership effect, our data also reveal a significant, direct benefit from AI adoption on innovation outcomes. However, contrary to the proposed hypothesis, the analysis indicates that AI does not function as a statistically significant moderator in the relationship between entrepreneurial leadership and innovation. This suggests that, within this context, AI operates as a parallel driver of innovation rather than an enhancer of the leadership’s effectiveness. The study provides a critical contribution to the literature by challenging the assumed interactive role of AI in leadership models within emerging economies. It offers actionable insights for leaders in technology firms, emphasizing the imperative of developing strong entrepreneurial leadership capabilities and pursuing strategic AI adoption as complementary, yet independent, pathways to achieving superior innovation. Full article

Despite the growing adoption of generative artificial intelligence (GenAI) tools in higher education, limited research has examined how future educators perceive and use these technologies in their academic practices. This study investigates the practices, perceived benefits, and challenges associated with the use of GenAI tools—such as ChatGPT—among undergraduate students enrolled in programs that confer teaching qualifications. Using a mixed-methods design, data were collected from 314 students from the Early Childhood Education, Philosophy, and Philology departments. The findings indicate that the majority of students use GenAI tools primarily for academic purposes, most commonly for information searching, data analysis, study advice, and exam preparation. Students reported several perceived benefits, including rapid access to information, time efficiency, improved comprehension of complex concepts, enhanced study organization, and support with assignments and research-related tasks such as summarizing or translating academic texts. At the same time, participants expressed notable concerns, particularly regarding over-reliance on AI, reduced personal effort, risks to academic integrity, diminished critical thinking, and weakened research skills. Additional challenges included misinformation, reduced creativity, improper use of AI-generated content, skill underdevelopment, and potential technological dependence. The study concludes that teacher education programs should systematically integrate AI literacy and responsible-use training to prepare future educators to address the pedagogical and ethical implications of GenAI in educational settings. Full article

Antonopoulou and Dare’s ongoing collaborative projects (Phi Books 2008: ongoing; Digital Dreamhacker 2013: ongoing) enact an open-ended, experimental set of slow ‘Fictioning’ practices and actions that involve performing, diagramming, or assembling to create or anticipate new modes of existence. In this paper, the authors use the visual essay form to evidence how their daily practices of drawing, writing, and exchanging, position art and the artist. These practices unfold without, in this case, the utilitarian, economic, and epistemic priorities and systems of reductive representation which underpin the extractive models of Generative AI and other ‘innovative’ intermediaries, systems which expedite content and regulate consumption in the cultural and creative industries and in ‘arts and humanities’ education. Focusing on their creative practices, Antonopoulou and Dare reposition commodified notions of productivity, creativity, and innovation, seeking what Haraway describes as a way ‘of making, thinking and worlding’ beyond the neoliberal imperatives of extracting profit from labour. Positioned within an era of escalating precarity combined with ecological and political instability driven by extractive colonialism, the temporality of collaboration and drawing over decades is proposed as an act of material resistance to art’s subsumption into the venture capitalist hype cycles. Such cycles are associated with an accelerating array of crises, discussed here. Full article

Introductory environmental science courses offer non-STEM students an entry point to address global challenges such as climate change and cryosphere preservation. Aligned with the International Year of Glacier Preservation and the Decade of Action for Cryospheric Sciences, this mixed-method, IRB-exempt study applied the Curriculum Redesign and Artificial Intelligence-Facilitated Transformation (CRAFT) model for course redesign. The project leveraged a human-centered AI approach to create anthropomorphized, place-based narratives for online learning. Generative AI is used to amend immersive virtual learning environments (VLEs) that animate glacial forces (water, rock, and elemental cycles) through narrative-driven virtual reality (VR) experiences. Students explored Iceland’s Jökulsárlón Glacier Lagoon via self-guided field simulations led by an imaginary water droplet, designed to foster environmental awareness and a sense of place. Data collection included a five-point Likert-scale survey and thematic coding of student comments. Findings revealed strong positive sentiment: 87.1% enjoyment of the imaginaries, 82.5% agreement on supporting connection to places, and 82.0% endorsement of their role in reinforcing spatial and systems thinking. Thematic analysis confirmed that anthropomorphic imaginaries enhanced emotional engagement and conceptual understanding of glacial processes, situating glacier preservation within geographic and global contexts. This AI-enhanced, multimodal approach demonstrates how narrative-based VR can make complex cryospheric concepts accessible for non-STEM learners, promoting early engagement with climate science and environmental stewardship. Full article