Search Results (471)

Background: this paper reports an investigation into the cognitive and emotional states of adolescents while learning from an LLM. It seeks to address a relative dearth in empirical evidence which might otherwise facilitate informed decisions being made by curriculum designers, school leaders and policy makers regarding the use of Generative AI, amidst the wider discourse about the effectiveness of AI in teaching and learning. Methods: in this paper, we analyze electrodermal activity (EDA) in the context of students’ scholastic engagement using LLMs in comparison to curated texts. In our 27-min-long experiment, we recorded the EDA of participants learning from both learning methods, for 8 min each. A quiz was also conducted to assess the effectiveness of the learning method. We collected 23 samples of EDA from the experiment, and 42 samples of quiz results. Results: we have found that learning with an LLM results in greater Skin Conductance Response (p = 0.09404), which is linked to more positive emotional valence, and lower Skin Conductance Level (p = 0.09473), which is linked to lower cognitive load, compared to curated texts. We also discovered that learning with an LLM correlates to a higher quiz result (p = 0.02053). While this suggests that learning and absorbing information with an LLM could be more effective than curated texts, results from self-reported data indicate that there are few perceived differences between the effectiveness of LLM and curated texts. Conclusions: this exploratory and preliminary study revealed empirical insights between LLM usage and learning effectiveness in situ via physiological indicators, in contrast to prior work that has adopted post hoc frames over the medium- to long-term. Full article

The growing emphasis on inclusive education highlights teachers’ attitudes and self-efficacy as interrelated yet distinct correlates of inclusive teaching. Building on prior literature that underscores their conceptual proximity, this study aimed to examine how teachers’ views on inclusion relate to their self-reported intentions and perceived self-efficacy for inclusive teaching. Given the cross-sectional, self-report design, the study addresses associations among attitudes, perceived self-efficacy, and intentions, rather than enacted inclusive practice. A cross-sectional survey was conducted with 323 Greek secondary special education teachers using three validated and culturally adapted instruments: the Attitudes toward Inclusive Education Scale (AIS), the Inclusive Classroom Teaching Intentions Scale (ITICS), and the Teacher Efficacy for Inclusive Practices Scale (TEIP). Pearson correlation analyses revealed strong within-instrument associations, indicating good internal coherence, and moderate cross-instrument associations, suggesting meaningful but not redundant relationships between attitudes, intentions, and self-efficacy. To further explore the latent structure, an Exploratory Factor Analysis (EFA) of AIS, ITICS, and TEIP items yielded a four-factor solution explaining 56.14% of the variance: Attitudes toward Inclusive Education, Intentions to Teach in Inclusive Classrooms, Self-efficacy for Behavior Management, and Self-efficacy for Collaboration and Professional Support. This study advances the field by clarifying how teachers’ attitudes, self-efficacy, and intentions covary, thereby informing the development of more targeted and theoretically grounded interventions. Full article

Education in entrepreneurship offers university students the opportunity to develop sound problem-solving and critical-thinking dexterity, which are crucial for navigating contemporary higher education. This research explores the opportunities and challenges of education in entrepreneurship within universities based in Lebanon, focusing on the role of fostering entrepreneurial alertness/awareness. This paper further examines how emerging technologies—specifically Generative Artificial Intelligence (Gen-AI)—impact these relationships. In spite of the increasing relevance of entrepreneurship, the results reveal constant limitations in students’ innovation and creativity, together with a lack of mentorship and training prospects for teachers. The study underlines the importance of integrating innovative systems, digital technological means, and sustainable education values to support SDG 4 (Quality Education) and reinforce learning quality environments. To empirically explore the relationships between the variables, the research uses a quantitative research design, using SmartPLS4 to investigate the structural paths between entrepreneurship education, student innovative behavior, entrepreneurial alertness, and the use of Gen-AI. Our data was collected from 197 participants through a validated survey scheme, together with insights received from instructors and students. The results indicate that instructors consider entrepreneurship education positively and recognize the potential of Gen-AI to improve teaching quality, encourage entrepreneurial alertness, and strengthen quality learning practices. Students also highlighted their requirement to acquire new skills and access new opportunities to enhance their decision-making abilities. Generally, the results/findings suggest that entrepreneurship education—emboldened by entrepreneurial alertness and moderated by Gen-AI—plays a vital role in improving students’ innovative behaviors and progressing SDG 4 through high-quality, inclusive, and transformative higher education. Full article

This study focuses on the transfer of architectural facade styles. Using the node-based visual deep learning platform ComfyUI, the system integrates the Flux Redux and Flux Depth models to establish a modular workflow. This workflow achieved style transfer of building facades guided by deep perception, encompassing key stages such as style feature extraction, depth information extraction, positive prompt input, and style image generation. The core innovation of this study lies in two aspects: Methodologically, a modular low-code visual workflow has been established. Through the coordinated operation of different modules, it ensures the visual stability of architectural forms during style conversion. In response to the novel challenges posed by generative AI in altering architectural forms, the evaluation framework innovatively introduces a “semantic inheritance degree” assessment system. This elevates the evaluation perspective beyond traditional “geometric similarity” to a new level of “semantic and imagery inheritance.” It should be clarified that the framework proposed by this research primarily provides innovative tools for architectural education, early design exploration, and visualization analysis. This workflow introduces an efficient “style-space” cognitive and generative tool for teaching architectural design. Students can use this tool to rapidly conduct comparative experiments to generate multiple stylistic facades, intuitively grasping the intrinsic relationships among different styles and architectural volumes/spatial structures. This approach encourages bold formal exploration and deepens understanding of architectural formal language. Full article

Over the past five years, generative AI has carved out a major role in architecture, especially in education and visual idea generation. Most of the time, the literature talks about AI as a tool, an assistant, or sometimes a co-creator, always highlighting efficiency and the end product in architectural design. There is a steady rise in empirical studies, yet the real impact on how young architects learn still lacks a solid theory behind it. In this systematic review, we dig into peer-reviewed work from 2015 to 2025, looking at how generative AI fits into architectural design education. Using PRISMA guidelines, we pull together findings from 40 papers across architecture, design studies, human–computer interaction and educational research. What stands out is a clear tension: on one hand, students crank out more creative work; on the other, their reflective engagement drops, especially when AI steps in as a replacement during early ideation instead of working alongside them. To address this, we introduce the idea of “AI sparring”. Here, generative AI is not just a helper—it becomes a provocateur, pushing students to think critically and develop stronger architectural concepts. Our review offers new ways to interpret AI’s role, moving beyond seeing it just as a productivity booster. Instead, we argue for AI as an active, reflective partner in education, and we lay out practical recommendations for studio-based teaching and future research. This paper is a theoretical review and conceptual proposal, and we urge future studies to test these ideas in practice. Full article

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education. Full article

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there is a lack of studies that assess its impact on students’ learning outcomes. Seemingly, Education 4.0 features are taken for granted, as if the technology in itself were enough to guarantee students’ learning, self-efficacy, and engagement. Seeking to address this lack, this study describes the implications of tailoring Education 4.0 tenets and digital learning in an engineering curriculum. Four case studies conducted in the last four years with 119 students are presented, in which technologies such as digital twins, a Modular Production System (MPS), low-cost robotics, 3D printing, generative AI, machine learning, and mobile learning were integrated. With these case studies, an educational methodology with active learning, hands-on activities, and continuous teacher support was designed and deployed to foster cognitive and affective learning outcomes. A mixed-methods study was conducted, utilizing students’ grades, surveys, and semi-structured interviews to assess the approach’s impact. The outcomes suggest that including Education 4.0 tenets and digital learning can enhance discipline-based skills, creativity, self-efficacy, collaboration, and self-directed learning. These results were obtained not only via the technological features but also through the incorporation of reflective teaching that provided several educational resources and oriented the methodology for students’ learning and engagement. The results of this study can help complement the concept of Education 4.0, helping to find a student-centered approach and conceiving a balance between technology, teaching practices, and cognitive and affective learning outcomes. Full article

This article introduces a framework for scientific and professional language training tailored for STEM (Science, Technology, Engineering and Mathematics) specialists, emphasising the integration of digital technologies and artificial intelligence (AI) in language education. The framework aims to develop students’ research communication skills and digital competencies, which are essential for effective participation in both national and international scientific discourse. The article discusses contemporary trends in STEM education, emphasising the importance of interdisciplinary approaches, project-based learning, and the utilisation of digital tools to boost language skills and scientific literacy. The article outlines the development and deployment of a digital platform aimed at supporting personalised and adaptive learning experiences, integrating various educational technologies and approaches. Empirical research conducted through a pedagogical experiment demonstrates the effectiveness of the framework, showing significant improvements in students’ academic and linguistic competencies across multiple modules. The findings highlight the importance of combining language training with STEM education to equip future engineers for the challenges of a globalised and digitalised professional world. This work reports on the “Enhancing Scientific and Professional Language Learning for Engineering Students in Kazakhstan through Digital Technologies” project conducted at Saginov Technical University (STU) in Kazakhstan and funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19678460). The research contributes to the ongoing discussion on improving language teaching in STEM fields, offering a framework that aligns with current educational demands and technological progress. Full article

Despite advances in AI pedagogical agents, research on developing creative instructional design remains limited, and how they affect pre-service teachers’ creative thinking—especially with metacognitive scaffolding—is unclear. Based on metacognitive and creativity investment theories, this study examines how such scaffolding in an AI agent fosters creative instructional design among pre-service preschool teachers, and whether critical thinking mediates this relationship moderated by AI dependency. A quasi-experimental design was used with 120 pre-service preschool teachers, who were assigned to the experimental group or the control group. Data on metacognitive awareness, AI dependency, critical thinking, and creative thinking were gathered through valid measuring instruments, and innovative curricula were evaluated by experts. The results show that the experimental group achieved much better scores on creative instructional design ideas after the test than the control group. The moderated mediation analysis revealed a critical thinking-mediated pathway that was moderated by AI dependency. In conclusion, AI pedagogical agents with metacognitive scaffolding (MAS-based) improved critical thinking and promoted deeper, more independent creative thinking, thus improving creative instructional design, through a pathway that is moderated by the degree of AI dependency. This study offers valuable theoretical and practical insights to cultivate creative teaching skills. Full article

The advent of Generative Artificial Intelligence (GenAI) has ushered in a transformative wave within the field of language education. However, the applications of GenAI are primarily in language teaching and learning, with assessment receiving much less attention. Drawing on task characteristics identified from a corpus of authentic prior tests, this study investigated the capacity of GenAI tools to develop a short College English Test-Band 4 (CET-4) listening test and examined the degree to which its content, concurrent, and face validity corresponded to those of an authentic, human-generated counterpart. The findings indicated that the GenAI-created test aligned well with the task characteristics of the target test domain, supporting its content validity, whereas sufficient robust evidence to substantiate its concurrent or face validity was limited. Overall, GenAI has demonstrated potential in developing listening tests; however, further optimization is needed to enhance their validity. Implications for language teaching, learning and assessment are therefore discussed. Full article

This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE is presented as a structured blueprint for future development and empirical validation, not as an implemented or evaluated system. Grounded in neuroscientific and educational research, E-MOTE seeks to strengthen teachers’ emotional awareness, teacher noticing, and social–emotional learning competencies. Rather than reporting empirical findings, this article offers a theoretically structured framework and an operational blueprint for the design of emotion-aware teacher training environments, establishing a structured foundation for future empirical validation. E-MOTE articulates three core contributions: (1) it clarifies the multi-layered construct of emotion-aware teaching by distinguishing between emotion detection, perception, awareness, and regulation; (2) it proposes an integrated AI–FACS–VR architecture for real-time and post hoc feedback on teachers’ perceptual performance; and (3) it outlines a staged experimental blueprint for future empirical validation under ethically governed conditions. As a design-oriented proposal, E-MOTE provides a structured foundation for cultivating emotionally responsive pedagogy and inclusive classroom management, supporting the development of perceptual micro-skills in teacher practice. Its distinctive contribution lies in proposing a shift from predominantly macro-behavioral simulation toward the deliberate cultivation of perceptual micro-skills through FACS-informed analytics integrated with AI-driven simulations. Full article

This study analyzes how Spanish universities integrate inclusion, accessibility, digital competence, and artificial intelligence (AI) into the professional development of university teaching staff, in a context marked by rapid digital transformation. The research addresses the lack of comparative evidence on how these key dimensions of contemporary higher education are articulated, or remain disconnected, across institutions. Using a mixed-methods design, 83 training courses delivered between 2020 and 2025 in 24 public and private universities were examined through qualitative analysis, coding matrices, and hierarchical cluster analysis. The study adopts an explicitly exploratory and typological approach, aimed at mapping institutional patterns rather than establishing causal explanations. The results reveal a highly heterogeneous and weakly cohesive training landscape. Inclusion appears primarily as a normative discourse with limited pedagogical depth; accessibility is frequently reduced to technical compliance; and AI (particularly generative AI) is incorporated from instrumental, efficiency-oriented approaches. Ethical dimensions, algorithmic bias, and digital accessibility are virtually absent. The hierarchical cluster analysis identifies four institutional profiles: technocentric without inclusion, analogically inclusive, advanced hybrid, and low-density training models. These patterns show how institutional orientations shape the professional development trajectories of university teaching staff. The study highlights the need for comprehensive faculty development strategies that integrate inclusion, accessibility, and responsible AI use, and offers a structured typological baseline for future research assessing impact on teaching practice and student experience. Full article

Generative artificial intelligence (GenAI) shows transformative potential in mathematics education. However, empirical findings remain inconsistent, and a systematic synthesis of its effects across distinct engagement dimensions is lacking. This preregistered meta-analysis (INPLASY2025110051) systematically reviewed 22 empirical studies (46 independent samples, N = 5232) published between 2023 and 2025. The results indicated that GenAI has a moderate positive impact on students’ mathematics learning outcomes (g = 0.534). Moderation analysis further revealed that the level of GenAI integration in teaching, sample size, and learning content are the primary factors influencing this effect. The study found that the effect was most pronounced under the creative transformation (CT) integration mode, was significant when applied to geometry learning, and was stronger in studies with small samples or small class sizes; collaborative learning approaches also significantly enhance these mathematics learning outcomes. By contrast, educational stage and intervention duration did not show significant moderating effects. The GRADE assessment indicated that while the overall evidence is supportive, the certainty of evidence is stronger for cognitive outcomes than for non-cognitive domains. The findings also offer a reference for future research on constructing a human–machine collaborative learning environment. Full article

This study investigates the factors influencing Chinese K-12 teachers’ adoption of generative artificial intelligence (GenAI) for instructional purposes by extending the Technology Acceptance Model (TAM) with pedagogical beliefs, perceived intelligence, perceived ethical risks, GenAI anxiety, and demographic moderators. Drawing on a theory-driven framework, survey data were collected from 218 in-service teachers across K-12 schools in China. The respondents were predominantly from urban schools and most had prior GenAI use experience. Eight latent constructs and fourteen hypotheses were tested using structural equation modeling and multi-group analysis. Results show that perceived usefulness and perceived ease of use are the strongest predictors of teachers’ intention to adopt GenAI. Constructivist pedagogical beliefs positively predict both perceived usefulness and intention, whereas transmissive beliefs negatively predict intention. Perceived intelligence exerts strong positive effects on perceived usefulness and ease of use but has no direct effect on intention. Perceived ethical risks significantly heighten GenAI anxiety, yet neither directly reduces adoption intention. Gender, teaching stage, and educational background further moderate key relationships, revealing heterogeneous adoption mechanisms across teacher subgroups. The study extends TAM for the GenAI era and highlights the need for professional development and policy initiatives that simultaneously strengthen perceived usefulness and ease of use, engage with pedagogical beliefs, and address ethical and emotional concerns in context-sensitive ways. Full article