Search Results (1,087)

Generative artificial intelligence (AI), especially large language models (LLMs) that can write and debug code, is changing how students approach programming work in engineering education. Unlike more open-ended conceptual or modeling tasks, programming fits closely with what these systems do well: generating syntax, fixing errors, building procedural logic, and completing code structures. Hence, programming coursework may be one of the areas in which AI changes performance patterns in a measurable way. This study examines whether that shift appears in actual student outcomes. Using a retrospective pre/post design, it compares results from a pre-AI period (2021–2022) with results from a post-AI period (2023–2025), when generative AI tools became widely available to students. The focal assessment is a comprehensive programming project graded with the same rubric across multiple sections and terms. Performance is evaluated through descriptive statistics, distributional comparisons, and mastery thresholds (≥80%). The post-AI period shows a rise in overall scores, along with strong clustering near the top of the scale. Lower- and middle-range scores become much less common, most students fall in the highest score band, and overall variability declines. These results suggest that generative AI acts as a procedural equalizer in programming contexts, referring to the role of generative AI in reducing performance differences by assisting with rule-based, syntax-driven, and execution-oriented aspects of tasks, thereby raising baseline outcomes while compressing variation among students. It appears to raise lower-end performance and make outcomes more consistent, but it also narrows the spread among stronger students and creates a ceiling effect. That pattern raises questions about assessment validity, skill differentiation, and what “mastery” means when AI can handle much of the procedural work. Using multi-term data from authentic online courses, this study adds empirical evidence to the growing literature on AI in engineering education and identifies programming coursework as a setting where generative AI may have already changed performance dynamics in a structural way. Full article

Telemedicine has become a routine component of healthcare delivery, creating a need for dedicated undergraduate training in teleconsultation-specific competencies. Although artificial intelligence (AI)-assisted educational systems have been proposed as scalable tools to support teleconsultation training, the evidence remains fragmented, and their educational role is not yet clearly defined. Objective: To map and critically synthesize empirical evidence on AI-assisted teleconsultation training systems used in undergraduate medical education, with attention to skill domains, system capabilities, and implementation considerations. Methods: A structured narrative review with transparent search and study selection procedures was conducted. Literature published between January 2019 and December 2025 was identified through searches of major bibliographic databases and supplementary semantic and citation-based sources. Studies involving undergraduate medical students and evaluating AI-assisted interventions targeting teleconsultation-related skills were included. Results: Eight empirical full-text studies met the final eligibility criteria and were included in the structured narrative synthesis. Across the included studies, AI-assisted systems tended to show favorable patterns in structured domains such as verbal communication, history-taking, and selected aspects of early clinical reasoning during virtual consultations. Evidence regarding nonverbal communication and empathic or relational skills was more limited and methodologically heterogeneous, and human-based simulation remained important in these domains. Students generally reported favorable perceptions of usability, accessibility, and psychological safety, although satisfaction and perceived realism were not uniformly superior to human-based approaches. AI-assisted systems also appeared scalable and potentially cost-efficient, particularly as preparatory or supplementary training modalities. Conclusions: Current evidence suggests that AI-assisted teleconsultation training systems may be useful as preparatory and supportive tools in undergraduate medical education, particularly for structured and repeatable components of remote consultation practice. However, the evidence base remains limited and heterogeneous, and these systems do not replace human-led training for relational, nonverbal, and context-sensitive competencies. Their educational value appears greatest within blended training models that align platform capabilities with specific teleconsultation skills. Full article

Learning path optimization is crucial in intelligent educational systems, with the core challenge of efficient multi-objective sequential decision-making under complex prerequisite constraints. To address the poor generalization of existing methods relying on fixed operator scheduling or handcrafted heuristics, this paper proposes a hyper-heuristic framework based on Dueling Deep Q-Network (Dueling DQN-HH), formulating operator selection as a sequential decision-making process for dynamic adaptive scheduling of low-level operators. The framework adopts priority-based encoding to unify learning path representation (decoupling the hyper-heuristic layer from the problem domain) and designs a composite reward mechanism integrating reward shaping, exploration incentives, and computational cost awareness to balance solution quality and efficiency. Additionally, it employs a dueling network architecture with prioritized experience replay to enhance policy learning stability. Experimental results show the proposed method outperforms representative baseline algorithms in solution quality, convergence stability, and computational efficiency. The framework demonstrates superior performance across multiple objectives, particularly in minimizing the total learning time (F_time), as validated on two heterogeneous datasets: MOOCCube (Computer Science) and PsyDataset (Psychology). Further ablation studies and operator evolution analyses verify its adaptive scheduling capability under different objectives and knowledge graph structures, demonstrating strong objective independence and cross-dataset generalization. Full article

This study investigates the design, development, and practical exploitation of an educational ontology for classical guitar instruction, within a semantically driven and application-oriented framework. The proposed approach aims to bridge the gap between formal knowledge representation and its functional use in real educational contexts. The ontology is developed using OWL in the Protégé environment and systematically models core pedagogical elements, including learning objectives, technical skills, instructional practices, and assessment processes, in alignment with the official curriculum. The semantic model is stored and managed as an RDF graph within a GraphDB repository, where it supports consistency checking and semantic querying through SPARQL. For application development, the ontological model is subsequently translated into a structured tabular schema suitable for the AppSheet low-code environment. Thus, GraphDB functions as a semantic validation and knowledge management layer, whereas the educational application operates on an application-oriented representation derived from the ontology rather than on a live RDF backend. The proposed three-tier architecture (Ontology–GraphDB–Application) demonstrates how Semantic Web technologies can support the transformation of abstract knowledge models into functional educational systems. The results highlight the capacity of ontology-driven approaches to enhance the organization, reusability, and pedagogical coherence of instructional knowledge, while enabling scalable and accessible application development through low-code technologies. The study contributes to the field of educational technology by providing a practical framework for integrating semantic knowledge representation into music education and laying a semantic foundation for future extensions toward adaptive and intelligent learning environments. Full article

The adoption of artificial intelligence (AI)-enabled technologies and information technology (IT) systems in entrepreneurship education has accelerated alongside the digital transformation of higher education. With a particular focus on gender-related disparities, this study examines how digital business modeling tools influence students’ entrepreneurial intentions. It conceptualizes digital tools along a continuum, ranging from non-AI solutions to AI-embedded and fully AI-driven systems. Data from 440 students taking part in entrepreneurial workshops using the AI-enabled digital tool KABADA served as the basis for empirical investigation. Changes in entrepreneurial intention and its key antecedents—attitude toward entrepreneurship, subjective norms, and perceived behavioral control—are examined by comparing the pre-workshop and post-workshop groups using structural equation modeling. According to the findings, the KABADA workshop has a statistically significant positive indirect effect on entrepreneurial intention, which is mainly mediated by perceived behavioral control. Significant gender differences are revealed by multi-group analysis: for female students, the main factor influencing entrepreneurial intention is perceived behavioral control, while for male students, the main factor is attitude toward entrepreneurship. These results emphasize the significance of IT systems that integrate guided user engagement with AI-based analytics to improve entrepreneurial self-efficacy, especially among women. Full article

Social and emotional learning (SEL) has become increasingly central to educational policy and lifelong development, while advances in robotics have opened new possibilities for supporting socio-emotional competencies through human–robot interaction. Despite the rapid growth of robot-assisted SEL research, this field remains fragmented, with limited understanding of its intellectual structure, thematic foci, and evolution. To address this gap, this study conducted a scientometric analysis of 241 publications indexed in Web of Science using bibliometric methods. Results indicate a steady growth trajectory, with research concentrated in a small number of core countries driving international collaboration. Influential publications and co-citation patterns reveal a strong foundation in autism-related interventions and child-centered social skill development. Thematic mapping shows that motor themes are dominated by soft skills, autism, and interaction design, while emotion recognition and affective computing form technically mature but specialized streams. Foundational concepts such as human–robot interaction and artificial intelligence remain central yet theoretically evolving. Emerging links between robotics, STEM, and project-based learning suggest ongoing pedagogical expansion. This study maps the intellectual and thematic structure of robot-assisted SEL, showing how robots are emerging as mediational agents in hybrid learning systems while revealing uneven integration and misalignments between technological capabilities and pedagogical foundations. Full article

Quantum computing is one of the most promising emerging technologies, and quantum machine learning (QML), as one of its key branches, is attracting growing interest for intelligent data processing in education. This study conducted a systematic review of QML in the context of Education 5.0 using the PRISMA 2020 methodology. A total of 48 peer-reviewed articles from Springer, Scopus, IEEE Xplore, PubMed, MDPI, arXiv, and APS were analyzed. The results indicate that QML has significant potential to enhance personalized learning, optimize educational data processing, support curriculum innovation, and foster the development of quantum-related competencies. Representative QML algorithms, including Quantum Support Vector Machines, variational quantum circuits, and quantum neural networks, are identified as key technological enablers for future educational applications. However, significant challenges remain, such as limited access to quantum infrastructure, lack of specialized curricula, hardware constraints, and the need for interdisciplinary training. Overall, this study highlights the growing relevance of QML for adaptive learning, learning analytics, and intelligent educational systems, while emphasizing the need for further empirical validation and scalable implementation in real educational environments. Full article

Artificial Intelligence (AI) is transforming higher education by introducing new learning dynamics based on automation, personalization, and data analytics. Within the framework of sustainable development, its integration into university education represents an opportunity to enhance the quality, accessibility, and resilience of educational systems, in alignment with Sustainable Development Goal 4 (SDG 4). Nevertheless, there remains limited empirical evidence regarding how the adoption of these technologies influences learning outcomes, particularly in engineering education contexts within developing countries. This study analyzes the adoption of artificial intelligence tools among engineering students and evaluates their impact on academic performance from the perspective of the Technology Acceptance Model (TAM). A structural model is proposed that examines the relationships among perceived ease of use, perceived usefulness, attitude toward AI, intention to use, and perceived academic learning. Based on a sample of 389 university students, the data were analyzed using Structural Equation Modeling (SEM). The results confirm that perceived ease of use significantly influences perceived usefulness, and that both variables positively impact attitudes toward artificial intelligence. In turn, attitude significantly influences usage intention, which is positively correlated with academic performance. Notably, attitude emerges as the primary predictor of learning, underscoring the central role of attitudinal factors in technology adoption. Furthermore, a slight negative effect of perceived ease of use on learning is identified, suggesting potential risks of superficial engagement or cognitive dependency in highly automated environments. These findings contribute to the literature by extending the TAM model to the analysis of sustainable learning, demonstrating that the adoption of artificial intelligence depends not solely on functional factors, but on interrelated cognitive, attitudinal, and behavioral processes. From a practical perspective, the study offers implications for higher education institutions, highlighting the need to promote a critical, reflective, and pedagogically guided use of artificial intelligence. This research provides empirical evidence from the context of engineering education in a developing country, contributing to an understanding of the role of artificial intelligence in building sustainable learning environments. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article

The cognitive environment in which students are immersed today is characterised by high exposure to images and content generated, selected and modulated by algorithmic systems. Visual education, therefore, runs the risk of becoming an accessory function of the system, serving to capture attention rather than to develop a critical eye; but visual education can also be used to question narrative linearity, destabilise established representations, and activate reflective processes. Artificial intelligence is a cognitive agent, and its use in visual teaching can help to deconstruct interpretative automatisms and open up spaces for more attentive and conscious learning. Full article

Natural language to structured query language (Text2SQL) is a critical task in intelligent question answering and database interaction. A fundamental challenge lies in achieving semantic symmetry between natural language expressions and database schemas, as well as behavioral symmetry between query generation and error correction. Traditional approaches often struggle with comprehending domain-specific concepts and dynamically adapting to user’s feedback, breaking the desired symmetry between system output and user intent. Thispaper presents EvoAgent-SQL as a lightweight engineering framework for private domain data querying based on low-cost lightweight models. The framework comprises three core agents: (1) the Schema Grounding Agent (SGA) establishes a symmetric mapping from natural language concepts to database fields; (2) the Execution Agent (EA) generates SQL queries; and (3) the Reflection Agent (RA) mirrors the EA’s outputs by analyzing errors and proposing corrections, forming a reflective symmetry loop. When ambiguity arises, user feedback is incorporated as a symmetry-breaking signal, which the RA uses to restore alignment through iterative evolution with a memory mechanism. We evaluate the framework on an education-domain dataset and provide a reproducibility plan for releasing sanitized schemas, natural language questions, and reference SQL while withholding confidential institutional records. Experimental results demonstrate that EvoAgent-SQL enhances query execution accuracy, achieving a $13.7\%$ reduction in the relative error residual after one evolution cycle and a $17.6\%$ cumulative reduction after two cycles, suggesting practical adaptability in domain-specific Text2SQL tasks. Full article

Background. Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing interest in its applications within pediatric dentistry. Given the unique clinical, developmental, and behavioral characteristics of pediatric patients, AI-based systems may offer valuable support in improving diagnosis, prevention, and treatment planning. Methods. A narrative review was conducted to synthesize current evidence on AI applications in pediatric dentistry. A comprehensive search strategy, including predefined keywords and free terms, was applied across multiple databases (Embase, Scopus, PubMed, and Web of Science) up to 1 January 2026. Reviews addressing AI-based technologies in pediatric dental care were selected and analyzed. Results. The available literature indicates that AI is being progressively applied across multiple domains of pediatric dentistry, although with varying levels of evidence. More extensively investigated areas include diagnostic imaging, caries detection, orthodontic assessment, and growth evaluation, where AI systems—particularly those based on machine learning and deep learning—have demonstrated high accuracy and reproducibility. Other emerging fields, such as remote monitoring, behavioral management, preventive strategies, and patient education, show promising potential but remain less explored. Overall, AI-based tools appear to enhance diagnostic support, enable early detection of oral conditions, and contribute to more personalized and efficient clinical workflows. Conclusions. AI represents a rapidly evolving adjunct in pediatric dentistry with the potential to improve clinical decision-making, preventive care, and patient management. Despite encouraging results, further validation in real-world settings, along with careful consideration of ethical, legal, and data-related challenges, is required to support its responsible integration into routine clinical practice. Full article

Higher education institutions are under increasing pressure to strengthen environmental education (EE) due to critical environmental challenges, while also addressing learner support, engagement, and instructional resource constraints. Recent advances in conversational artificial intelligence (AI), particularly generative AI systems based on large language models such as ChatGPT, enable new forms of human–machine cooperation and provide opportunities for interactive guidelines and individualized feedback. This study evaluates AI-supported EE compared with conventional classroom instruction using a quasi-experimental pre-test/post-test research design. Forty undergraduate students from a Libyan university were recruited and assigned to either the AI-supported EE group (n = 20) or a conventional classroom control group (n = 20). Both groups followed the same EE curriculum over eight weeks. Learning outcomes were assessed across environmental knowledge, attitudes, and environmentally responsible behavior using structured instruments. Paired-samples t-tests indicated statistically significant improvements within the AI-supported group across all outcomes (p < 0.05). However, between-group comparisons did not show statistically significant differences. Analysis controlling for baseline differences indicated a statistically significant group effect for knowledge (p < 0.05), while attitudes and behavior remained non-significant. These findings suggest that AI-supported learning may support EE learning for higher education. Full article

This study investigates how teacher-education undergraduates in Greece use, evaluate, and trust Artificial Intelligence (AI) in higher education, with particular attention to the gap between widespread adoption and limited epistemic trust. The topic is important because generative AI is rapidly entering universities, reshaping learning practices, academic integrity, and the legitimacy of knowledge, while learners often rely on systems whose outputs are not easily verifiable. The study focuses on future teachers because they are both current users of AI in higher education and likely future mediators of its use in school settings. Addressing this problem, the study contributes empirical evidence on how AI adoption relates to epistemic authority and institutional legitimacy within teacher education rather than across university students in general. A mixed-methods design was employed using a structured questionnaire completed by 363 teacher-education undergraduates from the University of Patras and the University of Ioannina in Greece; the sample was predominantly women (86.0%) and first-year students (92.6%). Quantitative responses were analyzed statistically, open-ended answers were examined thematically, and factor analysis was used to identify latent attitudinal dimensions. The findings indicate very high AI use in everyday life (92.6%) and study practices (81.3%), but only moderate trust: 1.4% reported complete trust and 12.1% generally trusted AI-generated answers. Six dimensions explained 61.73% of total variance, pointing to a layered attitudinal structure within this teacher-education population, consistent with an adoption–trust paradox and with the need for transparent, verifiable, human-supervised educational AI. The observed verification-based trust calibration may partly reflect an emerging pedagogical orientation toward source checking and responsibility for knowledge mediation, but given the strong concentration of first-year students, this should be interpreted as characteristic of early-stage teacher education rather than of university students more broadly. Full article

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education. Full article