Search Results (470)

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

This research explores the roles of perceived teacher support, L2 grit, and Foreign Language Enjoyment (FLE) in willingness to communicate (WTC) in English among Pakistani university students, thereby filling a contextual gap in Pakistani multilingual society. It utilized a quantitative cross-sectional design based on the WTC pyramid model by MacIntyre et al. and positive psychology. Adapted scales were used to gather data on 1050 multidisciplinary Pakistani English learners, who were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The main findings can be summarized as follows: (1) perceived teacher support had a small but significant direct effect on L2 WTC; (2) L2 grit had a strong and significant direct effect on L2 WTC; and (3) more importantly, FLE had a significant mediating effect. Indirectly, teacher support was the key factor in improving the L2 WTC, as evidenced by a significant increase in FLE. Though the impact of L2 grit was mostly direct, it was also indirect through FLE. This model explained 45.9 percent of the variation in L2 WTC. These findings highlight FLE, a favorable emotion, as the key channel through which environmental support (teacher support) and personal resilience (L2 grit) are translated into communicative willingness. The results confirm the inclusion of positive psychology into the multi-layered L2 WTC model, which emphasizes the importance of FLE in connecting cognition and emotion. This has important pedagogical implications for EFL/ESL contexts in Pakistan, where teachers should create engaging learning experiences, provide multidimensional support, and foster learners’ perseverance to enhance communicative interaction. Full article

Cybersecurity education requires practical activities such as malware analysis, phishing detection, and Capture the Flag (CTF) challenges. These exercises enable students to actively apply theoretical concepts in realistic scenarios, fostering experiential learning. This article introduces an innovative pedagogical approach relying on gamification in cybersecurity courses, combining technical problem-solving with human factors such as social engineering and risk-taking behavior. By integrating interactive challenges into the courses, engagement and motivation have been enhanced, while addressing both technological and managerial dimensions of cybersecurity. Observations from course implementation indicate that students demonstrate higher involvement when participating in supervised offensive security tasks and social engineering simulations within controlled environments. These findings highlight the potential of gamified strategies to strengthen cybersecurity competencies and promote ethical awareness, paving the way for future research on long-term cybersecurity learning outcomes. Full article

AI-powered tutors that interact with students in question-answering scenarios using large language models (LLMs) as foundational models for generating responses represent a potential scalable solution to the growing demand for one-to-one tutoring. In fields like mathematics, where students often face difficulties, sometimes leading to frustration, easy-to-use natural language interactions emerge as an alternative for enhancing engagement and providing personalized advice. Despite their promising potential, the challenges for LLM-based tutors in the math domain are twofold. First, the absence of genuine reasoning and generalization abilities in LLMs frequently results in mathematical errors, ranging from inaccurate calculations to flawed reasoning steps and even the appearance of contradictions. Second, the pedagogical capabilities of AI-powered tutors must be examined beyond simple question-answering scenarios since their effectiveness in math tutoring largely depends on their ability to guide students in building mathematical knowledge. In this paper, we present a study exploring the pedagogical aspects of LLM-based tutors through the analysis of their responses in math dialogues using feature extraction techniques applied to textual data. The use of natural language processing (NLP) techniques enables the quantification and characterization of several aspects of pedagogical strategies deployed in the answers, which the literature identifies as essential for engaging students and providing valuable guidance in mathematical problem-solving. The findings of this study have direct practical implications in the design of more effective math AI-powered tutors as they highlight the most salient characteristics of valuable responses and can thus inform the training of LLMs. Full article

This study examines how Artificial Intelligence (AI) can enhance Content and Language Integrated Learning (CLIL) through embodied, multimodal instruction in secondary Physical Education (PE). Drawing on Fernández Fontecha’s Content and Language Processing Sequence (CLPS) model, four AI-supported CLIL modules were designed and partially implemented in a Spanish secondary school. The exploratory, design-based study involved 25 students (aged 13–14) enrolled in second-year secondary education (2° ESO). Data were collected through a student perception survey and structured teacher observations to examine learners’ perceived content understanding, language use, engagement, and embodied participation in AI-supported CLIL tasks. Results indicate high levels of student engagement and positive perceptions of learning, particularly regarding vocabulary use, task comprehension, and the integration of physical movement with language use. Students reported that AI tools such as NaturalReader and Gliglish supported pronunciation practice, comprehension, and interactive language use when embedded within guided CLIL tasks. The findings highlight the pedagogical potential of AI as a mediating scaffold in embodied CLIL contexts, while underscoring the importance of teacher guidance and task design. The study contributes to emerging research on AI-enhanced CLIL by offering empirically grounded insights into the affordances and limitations of integrating AI in Physical Education. Full article

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i)Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii)Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate human–computer interaction (HCI) module, each bounded to a workflow-aligned role (e.g., Empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains. Full article

As artificial intelligence (AI) technologies and Virtual Exchange (VE) become increasingly embedded in higher education, AI-driven digital humans have begun to feature in design-oriented virtual international workshops, providing a novel context for examining learner behaviour. This study develops a structural model to examine the links between system support, interaction processes, self-efficacy, satisfaction, and international learning intention. Specifically, it investigates how perceived AI support, system ease of use, and interaction intensity influence students’ continuous participation in international learning through the mediating roles of learning self-efficacy, interaction quality, and satisfaction. Data were collected through an online questionnaire administered to undergraduate and postgraduate students who had participated in an AI-driven digital human–supported online international design workshop, yielding 611 valid responses. Reliability and validity analyses, as well as structural equation modelling, were conducted using SPSS 22 and AMOS v.22.0. The results show that perceived AI support, system ease of use, and interaction intensity each have a significant positive effect on learning self-efficacy and interaction quality. Both self-efficacy and interaction quality, in turn, significantly enhance learning satisfaction, which subsequently increases students’ intentions for sustained participation in international learning. Overall, the findings reveal a coherent causal chain: AI-driven digital human system characteristics → learning process experience → learning satisfaction → sustained participation intention. This study demonstrates that integrating AI-driven digital humans can meaningfully improve learners’ process experiences in virtual international design workshops. The results provide empirical guidance for curriculum design, pedagogical strategies, and platform optimization in AI-supported, design-oriented virtual international learning environments. Full article

This study explores how mixed reality (MR) game environments, enabled by sensor-based motion tracking and interactive visualization technologies, can be effectively integrated into blended learning to promote sustainability education. Using eight Macau bakeries as empirical cases, field investigations collected and categorized surplus bread samples, while carbon emission frameworks informed pedagogical design. Employing a multidimensional research methodology combining questionnaires and semi-structured interviews, the study delved into the intrinsic link between bread waste and carbon emissions. Through perceptual interaction design and task-oriented challenge modes within the MR environment, users were immersed in experiencing the pathway of sustainable behavioral impact. Post-instructional engagement with the MR game revealed that >90% of participants expressed strong affinity for the system design, and >85% perceived it as intuitively operable. Analysis of user feedback and performance data demonstrates the system’s potential to deliver solutions for reducing bread waste and carbon emissions. By establishing a replicable MR game framework and technical mechanisms, this research offers novel perspectives for future sustainability education studies in the field of behavioral mixed reality design. Full article

Background: Gamification is an innovative pedagogical strategy for improving learning outcomes, motivation, engagement, and knowledge retention. Nevertheless, evidence on the effectiveness of gamification remains heterogeneous. Methods: A systematic review was conducted. Searches were performed across PubMed/MEDLINE, CINAHL, PsycINFO, Scopus, Web of Science, Google Scholar, and grey literature (2010–2025). Eligible studies included quantitative, qualitative, and mixed-methods research involving undergraduate nursing students exposed to gamification interventions. Data extraction and quality assessment were independently performed using RoB-2, ROBINS-I, and JBI tools. Narrative synthesis was adopted due to the heterogeneity of interventions and outcome measures. Results: A total of 48 studies were included. Gamification strategies varied widely and included interactive quizzes, gamified flipped classroom models, serious games with explicit game elements, escape rooms, digital badges, and audience-response systems. For learning outcomes, most studies reported improvements in knowledge or performance, particularly when gamification included immediate feedback and repeated practice. While the knowledge retention was evaluated less frequently (12%), it was generally maintained or improved up to 2–4 weeks and across semester assessments. Strong positive trends of motivation and engagement were found across most studies, especially with competitive quizzes, missions, and narrative-based activities. Self-efficacy and satisfaction frequently improved, particularly in gamified simulations and team-based activities. Risk of bias was variable, with many quasi-experimental and descriptive studies limiting causal inference. Evidence certainty ranged from low to moderate according to GRADE criteria. Conclusions: Gamification is a promising educational approach in undergraduate nursing programs. Effects on long-term retention and practical skills remain less clear due to methodological variability and limited follow-up data. Future research focused on standardized outcome measures and longer follow-up intervals is required to consolidate evidence and guide educational policy. Protocol registered on PROSPERO (CRD420251117719). Full article

Cross-modal and immersive technologies offer new opportunities for experiential learning in early childhood, yet few studies examine integrated systems that combine multimedia, mini-games, 3D exploration, virtual reality (VR), and augmented reality (AR) within a unified environment. This article presents the design and implementation of the Solar System Experience (SSE), a cross-modal extended reality (XR) learning suite developed for preschool education and deployable on low-cost hardware. A dual-perspective evaluation captured both preschool teachers’ adoption intentions and preschool learners’ experiential responses. Fifty-four teachers completed an adapted Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) questionnaire, while seventy-two students participated in structured sessions with all SSE components and responded to a 32-item experiential questionnaire. Results show that teachers held positive perceptions of cross-modal XR learning, with Subjective Norm emerging as the strongest predictor of Behavioral Intention. Students reported uniformly high engagement, with AR and the interactive eBook receiving the highest ratings and VR perceived as highly engaging yet accompanied by usability challenges. The findings demonstrate how cross-modal design can support experiential learning in preschool contexts and highlight technological, organizational, and pedagogical factors influencing educator adoption and children’s in situ experience. Implications for designing accessible XR systems for early childhood and directions for future research are discussed. Full article

This article examines how students experience and build relational ties in online higher education. Using a hermeneutic phenomenological approach, it analyses students’ lived and desired relationships across four domains: the online campus, the degree programme, teachers, and peers. One hundred and forty-four students completed an open-ended questionnaire. Their narratives informed the Relational Proximity Matrix (RPM), a framework used to map connections and distinguish transformative, functional, and residual modes of proximity. Findings indicate strong affective and supportive ties among peers, whereas interactions with teachers and the online campus are often formal or instrumental. The study concludes that relational proximity, rather than access alone, depends critically on recognition, reciprocity, and pedagogical care. The RPM offers a heuristic orientation that may inform educational design and support educators and institutions in cultivating practices that enhance relational quality. Full article

Social robots are increasingly integrated into early childhood education, yet limited research exists examining preschoolers’ learning from robotic versus human demonstrators across embodied tasks. This study investigated whether children (aged between 3 and 6) demonstrate comparable performance when learning body-centered tasks from a humanoid robot compared to a human demonstrator. Sixty-two typically developing children were randomly assigned to a robot or a human condition. Participants completed three tasks: body part comprehension and production, body movement imitation, and emotion recognition from body postures. Performance was measured using standardized protocols. No significant main effects of demonstrator type emerged across most tasks. However, age significantly predicted performance across all measures, with systematic improvements between 3 and 6. A significant age × demonstrator interaction was observed for sequential motor imitation, with stronger age effects for the human demonstrator condition. Preschool children demonstrate comparable performance when interacting with a humanoid robot versus a human in body-centered tasks, though motor imitation shows differential developmental trajectories. These findings suggest appropriately designed social robots may serve as supplementary pedagogical tools for embodied learning in early childhood education under specific conditions. The primacy of developmental effects highlights the importance of age-appropriate design in both traditional and technology-enhanced educational contexts. Full article

Motion capture (MoCap) is increasingly recognized as a powerful multimodal immersive learning technology, providing embodied interaction and real-time motion visualization that enrich educational experiences. Although MoCap is gaining prominence within educational research, its pedagogical value and integration into computer animation training environments have received relatively limited systematic investigation. This review synthesizes findings from 17 studies to analyze how MoCap supports instructional design, creative development, and workflow efficiency in animation education. Results show that MoCap enables a multimodal learning process by combining visual, kinesthetic, and performative modalities, strengthening learners’ sense of presence, agency, and perceptual–motor understanding. Furthermore, we identified five key technical affordances of MoCap, including precision and fidelity, multi-actor and creative control, interactivity and immersion, perceptual–motor learning, and emotional expressiveness, which together shape both cognitive and creative learning outcomes. Emerging trends highlight MoCap’s growing convergence with VR/AR, XR, real-time rendering engines, and AI-augmented motion analysis, expanding its role in the design of immersive and interactive educational systems. This review offers insights into the use of MoCap in animation education research and provides a springboard for future work on more immersive and industry-relevant training. Full article

Technology plays an increasingly vital role in modern education, providing new opportunities to enhance engagement and conceptual understanding. Among emerging innovations, Augmented Reality (AR) enables interactive visualization that supports deeper comprehension of abstract and spatially complex concepts. This study aimed to evaluate the impact of AR technology integrated with the STEAM approach on fifth-grade students’ learning of geometric solids, focusing on spatial skills, motivation, and academic achievement. A quasi-experimental design was implemented, involving an experimental group that engaged in AR- and STEAM-based activities and a control group that followed traditional instruction. Results indicated significant improvement in geometry test performance within the experimental group (p < 0.001) and higher post-test performance compared to the control group (p = 0.005). Although motivation scores were higher in the experimental group, the difference was not statistically significant (p = 0.083), suggesting a positive trend that merits further exploration with a larger sample. Overall, the findings highlight the pedagogical potential of integrating AR and STEAM approaches to support engagement and conceptual understanding in geometry education. Full article

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide. Full article