Search Results (68)

In an era characterized by the deep integration of digitalization and intelligent technologies, generative artificial intelligence (GAI) is reshaping the ecology of higher education in unprecedented ways. Owing to its inherent complexity, practice-oriented nature, and interdisciplinary characteristics, undergraduate architectural education can no longer be fully supported by traditional pedagogical models in response to emerging demands such as sustainable design, digital twins, and intelligent construction. Based on cross-sectional survey data from 1121 architecture undergraduates across eight universities in Wuhan, Hubei Province, this study proposes the Generative AI-enabled Learning Reshaping Association Model (GAI-LRM) and employs partial least squares structural equation modeling (PLS-SEM) to examine the statistical relationships between the variables. The generative AI tools investigated include text-generation tools such as Kimi AI, Doubao and Seedance, as well as image and design generation tools like Midjourney, Stable Diffusion, Forma AI, and ArkoAI. The results indicate that system-generated content quality, system quality, and task–technology fit are all significantly and positively associated with learning reshaping. Learning relationship reshaping and cognitive flexibility demonstrate positive indirect associations within the relevant pathways, whereas technology dependence shows a negative indirect association. Furthermore, there is a significant association between students’ foundational knowledge in the subject and certain variables. These findings reveal the multifaceted connections between the application characteristics of generative AI and changes in the learning processes of architecture undergraduates; they provide empirical insights for optimizing human–AI collaborative learning, critical design reviews, and tiered instruction in design studios at universities in Wuhan, while also establishing a theoretical framework for future cross-regional, longitudinal, and experimental studies. We situate these findings within a core framework of contemporary architectural scholarship, where mainstream architectural education continues to privilege image-driven representation and adherence to established stylistic paradigms, even as a parallel scientific research movement harnesses artificial intelligence to reshape fundamental design principles. Viewed from this perspective, our results reveal not only the current state of technology adoption but also the underlying mechanisms at play. Specifically, technological reliance diminishes cognitive flexibility, while deep disciplinary literacy constitutes the critical differentiator between uncritical replication and deliberate application. Consequently, we argue that architectural education should not merely incorporate GAI within existing visual paradigms but should instead steer it toward science-based, human-centric design principles. Full article

Generative artificial intelligence (GAI) enables students to offload cognitive tasks to an external system, yet the consequences of such cognitive offloading for the development of critical thinking—a core dimension of human intelligence—remain underexplored. Drawing upon cognitive offloading theory and distributed cognition theory, this study investigates the heterogeneity of critical thinking outcomes among Chinese university students who use GAI, focusing on how different patterns of human–AI collaboration relate to cognitive autonomy relinquishment. A questionnaire survey was administered to 353 university students across multiple provinces in China. Cluster analysis and regression analysis were employed to identify distinct user profiles and to examine predictors of critical thinking gains and cognitive autonomy. Four distinct user profiles emerged, ranging from “simple Q&A users” (25.2%) to “critical co-thinkers” (15.6%). Learning motivation was the strongest predictor of both critical thinking gains (β = 0.42) and lower cognitive autonomy relinquishment (β = −0.35). Notably, offloading depth positively predicted cognitive autonomy relinquishment (β = 0.25), revealing a paradoxical pattern: sophisticated GAI use was associated with greater dependence. A “high depth–high dependence” subgroup (25.8%) was identified, disproportionately composed of female students and Information and Communication Technology (ICT) majors. The findings challenge the assumption that deeper GAI engagement automatically yields cognitive benefits. Because all constructs were measured through self-report, the findings are interpreted as reflecting students’ perceptions of their cognitive behaviors and abilities; the methodological implications of this design are discussed in detail. Educational interventions should prioritize metacognitive training over technical skill development to ensure that cognitive offloading enhances rather than undermines critical thinking. Full article

This study aims to investigate the integration of generative artificial intelligence (GAI) and advanced large language models (LLMs) in financial sentiment research, focusing on improving the accuracy and robustness of financial sentiment classification from investor-generated textual data. The research employs advanced large language models, including XLNet, FinBERT, T5, Gemma-7B, Llama-2, and Llama-3, specifically fine-tuned to address the intricacies of financial language. We utilize generative AI models, such as GPT-4, GPT-3.5, and GPT-2, for data augmentation to mitigate scarcity. The fine-tuned Gemma-7b model proved to be the most successful, with a greater Success Rate (S-rate). The Gemma-7b model showed significant enhancements in performance after fine-tuning, highlighting its capacity to grasp the intricacies of financial emotion. This methodology provides a robust framework for financial sentiment classification and supports the extraction of meaningful sentiment signals from financial text. The results demonstrate the effectiveness of advanced LLMs for financial sentiment analysis and highlight their potential for supporting future research and analytical applications in financial text mining. Full article

Unmanned aerial vehicles (UAVs) have been widely used in recent years in various applications thanks to advances in communication, Internet of Things, and electronics. Despite the advantages they offer, there have been reports of cybersecurity attacks, which represent serious threats to their operations. Classic cryptographic-based solutions and traditional intrusion detection approaches generally struggle to deal with these attacks due to their adaptive and evolving nature. In this context, artificial intelligence (AI) models emerged as potential solutions that hold great promise in addressing these types of attacks. However, most related surveys presented a fragmented picture of the state of the art, failing to cover all sub-types of AI models, and often did not follow structured taxonomies for describing the literature. In this paper, we bridge this gap by proposing a novel and comprehensive survey inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, defining the search strategy, inclusion and exclusion criteria, selection process, and classification. We also present a cross-dimensional taxonomy that classifies UAV security research according to the type of AI model, the cyber attacks it thwarts, and the related security properties it enforces. This taxonomy does not stop at describing machine learning (ML) and deep learning (DL) approaches but also examines federated learning (FL), reinforcement learning (RL), graph neural network (GNN), and generative AI (GAI). We also classify the threat vector according to the layer in the UAV functional stack where the attack takes place. In addition, we describe the datasets, tools, and evaluation metrics that were mostly used in the literature. Our survey analyzes the common uses of each AI model type in UAV security and discusses its strengths, limitations, and deployment readiness. The outcome of our taxonomy is a quantitative and qualitative analysis providing quantifiable metrics on the covered security properties per model type. We conclude the paper by discussing the key open challenges and future directions in the field. We intend for this survey to serve as a reference for cybersecurity researchers and practitioners who tackle UAV security using AI. Full article

The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students’ perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) theory, this study investigates how undergraduate students’ use of GAI relates to perceived English learning achievement and under what conditions these associations are amplified. Using covariance-based structural equation modeling (CB-SEM), data from 537 undergraduate students across five public universities in China were analyzed. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and artificial intelligence self-efficacy significantly predict GAI use. In turn, use behavior mediates their relationships with perceived English learning achievement. Task–Technology Fit further moderates the link between use behavior and learning achievement, with stronger associations observed when GAI functionalities are perceived as closely aligned with task requirements. These results highlight the importance of use behavior and task alignment in explaining how GAI is associated with students’ perceived English learning achievement and extend technology acceptance research within AI-supported language learning contexts. Full article

The integration of Generative Artificial Intelligence (GAI) into software design tools has transformed the early stages of mobile application development, particularly prototype creation from natural-language prompts. This study evaluates the usability and effectiveness of GAI-assisted prototyping tools for generating high-fidelity mobile application prototypes. A controlled laboratory usability study was conducted in which undergraduate Information Technology Engineering students used and evaluated four widely adopted prototyping platforms: Figma, Uizard, Visily, and Stitch. Participants employed these tools to recreate mobile interfaces corresponding to the interaction model of the Duolingo application. The System Usability Scale (SUS) was used to assess perceived usability and effectiveness from the users’ perspective. The results indicate that all evaluated tools enabled rapid prototype generation; however, significant differences emerged in usability, structural fidelity, and perceived control. Figma and Stitch achieved the highest usability scores and demonstrated greater alignment with the reference prototype (82.86 and 80.36, respectively). Visily achieved a favorable usability score (78.57), while Uizard obtained a moderate score (67.14). Although Uizard and Visily exhibited strong automation capabilities and faster initial generation, their outputs required additional manual refinement to achieve higher fidelity and customization. Participant feedback emphasized the importance of output quality, responsiveness, and foundational design knowledge in achieving satisfactory results. Overall, the findings suggest that current GAI-based prototyping tools are effective and valuable in real-world software development contexts. However, their effectiveness appears closely related to the degree of user control, responsiveness, and the ability to iteratively refine AI-generated interface components. Full article

The advancement of generative artificial intelligence (GAI) in healthcare is driving a transition from dialogue-based medical chatbots to workflow-embedded clinical AI agents. These agentic systems incorporate persistent state management, coordinated tool invocation, and bounded autonomy, enabling multi-step reasoning within institutional processes. As a result, traditional response-level evaluation frameworks are insufficient for understanding system behavior. This review provides a conceptual synthesis of the evolution from conversational systems to agentic architectures and proposes a system-level modeling framework for ethical clinical AI agents. We identify core architectural dimensions, including autonomy gradients, state persistence, tool orchestration, workflow coupling, and human–AI co-agency, and examine how these features reshape bias propagation pathways, error cascade dynamics, trust calibration, and accountability structures. Emphasizing that ethical risks emerge from longitudinal system interactions rather than isolated outputs, we argue for embedding fairness constraints, transparency mechanisms, and lifecycle governance directly within AI design. By outlining trajectory-level evaluation strategies, equity-aware development approaches, collaborative oversight models, and adaptive regulatory frameworks, this paper establishes a foundation for the responsible and trustworthy integration of agentic AI in healthcare. Full article

The integration of generative artificial intelligence (GAI) into digital learning environments is a profound socio-technical transformation. While GAI promises enhanced accessibility and efficiency, it simultaneously obscures the human creativity and intellectual labour that underpins digital knowledge production. This opacity limits creators’ visibility into how their work is used, evaluated, and monetised. This review application work investigates how several leading large language models, including ChatGPT (GPT-4o), Gemini (1.5 Flash), and DeepSeek (V3), interact with a creative platform hosting over 300 original essays, poems, and artworks from various human creatives. Our review reveals that despite clear evidence of models engaging with original materials, standard platform analytics of the average creative record no attribution, referrals, or traceable interaction from their end, rendering creators’ labour invisible. This compels critical examination of knowledge provenance and power within AI-mediated education. To address this, we propose a socio-technical framework, Chujoyi-TraceNet, not as a technical fix, but a mechanism to re-centre ethics, justice, and recognition in digital governance. By integrating real-time tracking, blockchain-enabled licensing, and metadata watermarking, Chujoyi-TraceNet operationalises the principles of equitable attribution. This study argues for a re-imagining of digital ecosystems in education, one that links the technical act of attribution to broader debates on digital labour, platform ethics, and the pursuit of social justice, thereby contributing to more democratic and accountable learning media in the era of Industry 4.0 and 5.0. Full article

As artificial intelligence (AI) changes educational practices, understanding what sustains pre-service teachers’ generative AI use beyond initial adoption becomes important. However, existing research mainly focuses on initial acceptance rather than continuance intention, which is a more realistic indicator for sustainable technology integration. This study drew on an integrated framework including psychological (GAI anxiety, GAI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) to examine pre-service teachers’ continuance intention toward GAI in future teaching. Survey data from 549 Chinese pre-service teachers were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results showed that GAI self-efficacy had the strongest positive associations with both perceived ease of use and perceived usefulness. GAI anxiety negatively influenced both perceptions. However, facilitating conditions did not significantly relate to perceived usefulness. The fsQCA identified six configurational pathways clustered into the following three patterns: intrinsic value driven, efficacy capability driven, and external support driven. These findings suggest that teacher education programs should prioritize building GAI self-efficacy and supportive peer environments and not focus solely on infrastructure provision. Full article

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

In recent years, the integration of advanced methods in medical imaging has become a major topic of interest due to its potential to enhance diagnostic accuracy, improve clinical efficiency, and increase specialists’ confidence in Artificial Intelligence (AI)-based decision-making. This paper explores the synthesis of Explainable AI (XAI) and Generative AI (GAI) in medical imaging, highlighting the advantages and challenges of these emerging technologies. The objective of this paper is to explore how the combined use of XAI and GAI contributes both to interpretability and to diagnostic accuracy. This research represents a systematic literature review conducted in accordance with PRISMA 2020, based on searches carried out in the PubMed, Scopus, IEEE Xplore, MDPI and ScienceDirect databases. Thus, a comprehensive overview of the integration of XAI and GAI in medical imaging is presented, based on recent studies and validated clinical applications. The advantages of combining transparency and data amplification in diagnostic models are highlighted, demonstrating their complementary roles in improving diagnosis using medical imaging. Ongoing challenges in clinical adoption are also emphasised, including interpretability and the need for validated assessment metrics. Beyond technological benefits, the paper also underlines the importance of ethical and legal considerations in the use of XAI and GAI in medical imaging. Based on the detailed analysis of the investigated studies, the paper also proposes a visual and architectural system concept intended for medical imaging, oriented towards research into the development of a unified system capable of detecting multiple types of pathologies. This research provides a detailed perspective on how XAI and GAI can revolutionise medical imaging by optimising data interpretation, enhancing human-AI collaboration, and increasing patient safety. Full article

Background: Artificial intelligence (AI) is transitioning from proof-of-concept prototypes to clinically utilized tools in orthopedics. The key translational question is whether AI will replace surgeons or, more realistically, augment human expertise. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Web of Science, and Google Scholar (completed 31 January 2026). Peer-reviewed English-language studies that utilized AI for orthopedic clinical problems were eligible. To synthesize the 73 included papers without forced quantitative pooling, evidence was qualitatively charted and organized using a four-axis framework: clinical task, data modality, validation maturity, and intended user/setting. Results: The evidence base was dominated by retrospective, imaging-centered AI studies (predominantly LOE III). Radiograph-based fracture detection and automated measurements were frequently reported to achieve high discrimination, though performance degraded in complex or “edge” cases. Predictive models for arthroplasty and spine outcomes demonstrated variable actionability and inconsistent reporting of calibration. Common translational barriers across subspecialties included limited external validation, dataset shift, and a scarcity of prospective impact studies. Conclusions: Current evidence supports an augmentation paradigm rather than a replacement paradigm. AI acts as a “co-surgeon,” improving triage and standardizing quantification. However, safe clinical translation requires representative external validation, rigorous failure analysis, and human-in-the-loop workflows where surgeons retain ultimate accountability. Full article

This study investigates how integrating the 4PADAFE instructional design methodology with generative artificial intelligence (GAI) tools helps develop innovative, pedagogically sound digital learning environments in higher education. To meet the demand for scalable and flexible instructional models, 4PADAFE offers a seven-phase, iterative framework that connects pedagogical goals with the creative use of AI-powered tools. Using a qualitative exploratory approach, 20 Systems Engineering students applied the methodology to collaboratively create a four-week Massive Open Online Course (MOOC) titled “Generative Artificial Intelligence Tools for University Teaching.” They utilized ChatGPT, DALL·E, and Gamma to produce educational materials without direct input from subject-matter experts. Data collection included semi-structured interviews, non-participant observation, and analysis of student-created artifacts. The findings revealed increased learner autonomy, creativity, and digital skills, along with more efficient instructional design processes supported by prompt engineering and real-time feedback. The structured 4PADAFE framework helped participants align AI-generated content with specific learning outcomes while maintaining ethical safeguards. This study concludes that, with proper guidance and a systematic framework, students with technical backgrounds can serve as effective instructional designers, demonstrating the potential of combining structured methodologies and GAI to democratize high-quality course development in digital higher education. Full article

The accelerated spread of generative artificial intelligence (GAI) is transforming firm-level production processes and performance outcomes, yet its implications for energy efficiency remain understudied. Using panel data on Chinese listed companies, this study examines how advances in GAI influence firms’ energy intensity (EI). The empirical evidence indicates that greater development of GAI is associated with a significant reduction in firms’ EI. Mechanism analyses reveal that GAI decreases EI by promoting firms’ human capital structure and dynamic capabilities. Furthermore, the negative relationship between GAI development and EI is larger when stronger informal environmental regulations are in place and for firms that have better internal environmental governance. This study extends prior research on the environmental implications of traditional AI and enriches the literature on GAI by moving beyond productivity and innovation outcomes. Our findings enhance the understanding of how dynamic technology evolution in AI reshapes the landscape of energy consumption and have important implications for efforts to improve energy efficiency under digital transformation. Full article

Generative Artificial Intelligence (GAI) is rapidly reshaping pedagogical practices and offering new opportunities to advance sustainability within higher education. This study investigates the extent to which postgraduate students utilize GAI to support Sustainable and Environmentally Responsible Teaching Practices (SERTPs), and examines whether this use varies across demographic, academic, and technological characteristics. A descriptive quantitative design was employed, involving 310 postgraduate students from the College of Education at King Khalid University. Data were collected using a validated and highly reliable instrument measuring five dimensions of GAI-supported sustainable teaching. Descriptive and inferential analyses, including t tests, one-way ANOVA, and LSD post hoc comparisons, were conducted. The findings reveal that postgraduate students demonstrate a moderate overall level of GAI use in advancing SERTPs, with the highest engagement occurring in the promotion of sustainable educational practices. Significant differences were only found in relation to students’ levels of technology use and students’ levels of GAI use, indicating that frequent and sophisticated engagement with AI tools is the strongest predictor of sustainable teaching practices. No significant differences emerged across gender, age, academic department, program level, or specialization. The study highlights the need for targeted training and institutional strategies that enhance students’ AI proficiency, thereby enabling GAI to serve as a catalyst for environmentally responsible and sustainable teaching practices in higher education. Full article