Search Results (1,517)

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

This study investigates the effectiveness of contact and online biology teaching by assessing student performance and gathering perceptions from students, teachers, and parents. Conducted in autumn 2021 with 3035 students, 124 biology teachers, and 719 parents, this study combined post-instruction assessments of student performance in knowledge reproduction and conceptual understanding with questionnaires examining perceptions of contact and online biology teaching effectiveness across students, teachers, and parents. To investigate how various teaching-related factors influence perceived understanding of biological content, we applied a CHAID-based decision tree model to questionnaire responses from students, teachers, and parents. Results indicated that students value engaging, flexible instruction, sufficient time to complete tasks and support for independent thinking. Teachers emphasized their satisfaction with teaching and efforts to support student understanding. In contact lessons, students preferred problem-solving, teacher guidance, and a stimulating environment. In online learning, they preferred low-stress, interesting lessons with room for independent work. Parents emphasized satisfaction with their child’s learning and the importance of a focused, stimulating environment. This comparative analysis highlights the need for student-centered, research-based biology teaching in both formats, supported by teachers and delivered in a motivating environment. The results offer practical insights for improving biology instruction in different teaching modalities. Full article

Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness. Full article

Advances in neuroscience have improved the understanding of cognitive, emotional, and social processes involved in learning. Simultaneously, technologies such as artificial intelligence, augmented reality, and gamification are transforming educational practices. However, their integration into formal education remains limited and often misapplied. This study aims to evaluate the impact of technology-supported neuroeducational models on student learning and well-being. A systematic review was conducted using PubMed, the Web of Science, ScienceDirect, and LILACS, including open-access studies published between 2020 and 2025. Selection and methodological assessment followed PRISMA 2020 guidelines. Out of 386 identified articles, 22 met the inclusion criteria. Most studies showed that neuroeducational interventions incorporating interactive and adaptive technologies enhanced academic performance, intrinsic motivation, emotional self-regulation, and psychological well-being in various educational contexts. Technology-supported neuroeducational models are effective in fostering both cognitive and emotional development. The findings support integrating neuroscience and educational technology into teaching practices and teacher training, promoting personalized, inclusive, and evidence-based education. Full article

It is well-established that principal leadership significantly influences student learning in developed countries, yet much less is known about how leadership practices manifest in complex systems like China’s, where rapid modernization intersects with deep-rooted educational traditions. In particular, Chinese principals face multiple challenges in balancing the implementation of educational reform policies, high parental expectations, and their own educational ideology, all within limited resources. The current study examines these challenges in Shenzhen, a city which typically manifests them through its rapid development. Specifically, we took a phenomenographic approach and interviewed the principals and staff from five prestigious primary schools to extract the key components behind the diverse school leaders’ styles and practices. Results showed that, the Chinese leadership practice model consists of five key components: mission setting, infrastructure reconstruction, teacher development, learning improvement, and educators’ networking. Although the first four components in this model align with established theories in developed countries, networking was identified as a distinctive and critical element for securing resources and fostering collaboration. These findings may broaden the scope of leadership theories and underscore the need to contextualize leadership practices based on local challenges and dynamics. It also offers practical insights for school leaders on navigating challenges to improve teacher and student outcomes. Full article

Background/Objectives: Legislative changes in educational systems have influenced how student learning is understood and promoted. In physical education (PE), there has been a shift from behaviorist models to more holistic approaches. In this context, physical literacy (PL) is presented as an emerging pedagogical model in school PE, aimed at fostering students’ motor competence in a safe, efficient, and meaningful way. The aim of this study is to analyze the origins, foundations, methodological elements, and educational value of PL, highlighting its potential to promote holistic and inclusive learning as the basis for an emerging PL model. Methods: A narrative review was conducted through a literature search in the Web of Science, PubMed, Scopus, and SportDiscus databases up to June 2025, focusing on scientific literature related to PL and PE. The analysis included its historical background, philosophical and theoretical foundations, and the key methodological elements and interventions that support its use as a pedagogical model. Results/Discussion: The findings indicate that the PL model can be grounded in key principles, such as student autonomy, teacher training, connection with the environment, inclusion, and collaboration. Additionally, motivation, enjoyment, creativity, and continuous assessment are identified as essential components for effective implementation. Moreover, this model not only guides and supports teachers in the field of PL but also promotes comprehensive benefits for students at the physical, cognitive, affective, and social levels, while encouraging increased levels of physical activity (PA). Conclusions: PL is understood as a dynamic and lifelong process that should be cultivated from early childhood to encourage sustained and active participation in PA. As a pedagogical model, PL represents an effective tool to enhance student learning and well-being in PE classes. Full article

Image captioning plays a crucial role in aligning visual content with natural language, serving as a key step toward effective cross-modal understanding. Transformer has become the dominant language model in image captioning. Existing Transformer-based models seldom highlight important features from multiple views in the use of self-attention. In this paper, we propose MFEAM, an innovative network that leverages the multi-view feature enhanced attention. To accurately represent the entangled features of vision and text, the teacher model employs the multi-view feature enhanced attention to guide the student model training through knowledge distillation and model averaging from both visual and textual views. To mitigate the impact of excessive feature enhancement, the student model divides the decoding layer into two groups, which separately process instance features and the relationships between instances. Experimental results demonstrate that MFEAM attains competitive performance on the MSCOCO (Microsoft Common Objects in Context) when trained without leveraging external data. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Semi-supervised object detection (SSOD) in remote sensing is challenged by the accumulation of pseudo-label errors in complex scenes with dense objects and high intra-class variability. While teacher–student frameworks enable learning from unlabeled data, erroneous pseudo-labels such as false positives and missed detections can be reinforced over time, which degrades model performance. To address this issue, we propose the Error-Mitigation Teacher (EMT), a unified framework designed to suppress error propagation during SSOD training. EMT consists of three lightweight modules. First, the Adaptive Pseudo-Label Filtering (APLF) module removes noisy pseudo boxes via a second-stage RCNN and adjusts class-specific thresholds through dynamic confidence filtering. Second, the Confidence-Based Loss Reweighting (CBLR) module reweights training loss by evaluating the teacher model’s ability to reconstruct its own pseudo-labels, using the resulting loss as an indicator of label reliability. Third, the Enhanced Supervised Learning (ESL) module improves class-level balance by adjusting supervised loss weights according to pseudo-label statistics. EMT demonstrates consistent performance gains over representative state-of-the-art SSOD methods on DOTA, DIOR, and SSDD datasets. Notably, EMT achieves a 2.9% absolute mAP₅₀ improvement on DIOR using only 10% of labeled data, without incurring additional inference cost. These results highlight EMT’s effectiveness in improving SSOD for remote sensing. Full article

In alignment with global educational trends, Australia has adopted a pluralistic approach to initial teacher education (ITE), encompassing traditional university-based programs, employment-integrated models and vocational training routes. This diversification of pathways has emerged as a strategic response to persistent workforce challenges, including chronic shortages, uneven distribution of qualified educators, and limited demographic diversity within the profession. Rather than supplanting conventional ITE models, these alternative pathways serve as complementary options, broadening access and enhancing system responsiveness to evolving societal and educational needs. The rise in non-traditional routes represents a deliberate response to the well-documented global teacher shortage, frequently examined in comparative educational research. Central to their design is a restructuring of traditional program elements, particularly duration and delivery methods, to facilitate more flexible and context-sensitive forms of teacher preparation. Such approaches often create opportunities for individuals who may be excluded from conventional pathways due to socioeconomic constraints, geographic isolation, or non-linear career trajectories. Significantly, the diversity introduced by alternative entry candidates has the potential to enrich school learning environments. These educators often bring a wide range of prior experiences, disciplinary knowledge, and cultural perspectives, contributing to more inclusive and representative teaching practices. The implications for student learning are substantial, particularly in disadvantaged communities where culturally and professionally diverse teachers may enhance engagement and academic outcomes. From a policy perspective, the development of flexible, multifaceted teacher education pathways constitutes a critical component of a sustainable workforce strategy. As demand for qualified teachers intensifies, especially in STEM disciplines and in rural, regional and remote areas, the role of alternative pathways is likely to become increasingly pivotal in achieving broader goals of equity, quality and innovation in teacher preparation. Full article

In the field of image dehazing, many deep learning-based methods have demonstrated promising results. However, these methods often neglect crucial frequency-domain information and rely heavily on labeled datasets, which limits their applicability to real-world hazy images. To address these issues, we propose a semi-supervised image-dehazing network based on a trusted library (WTS-Net). We construct a dual-branch wavelet transform network (DBWT-Net). It fuses high- and low-frequency features via a frequency-mixing module and enhances global context through attention mechanisms. Building on DBWT-Net, we embed this backbone in a teacher–student model to reduce reliance on labeled data. To enhance the reliability of the teacher network, we introduce a trusted library guided by NR-IQA. In addition, we employ a two-stage training strategy for the network. Experiments show that WTS-Net achieves superior generalization and robustness in both synthetic and real-world dehazing scenarios. Full article

This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching. Full article

The rapid secularisation of society has made the work of religious education in Catholic secondary schools increasingly difficult. Contemporary RE teachers are often faced with wildly disparate knowledge and interest levels in their classrooms, to say nothing of their own religiosity. Many systems focus on new curricula, new forms of professional development opportunities, or tertiary courses as a means of enriching what is happening in the classroom. This article examines the approach developed in a small rural diocese in accompanying the RE teachers working in its five secondary schools. It is an accompaniment model that is grounded in the theological and pedagogical insights of Luigi Giussani and adapted to the realities of contemporary education in an Australian setting. The results are a surprising proliferation of enrichment and innovation that can be immediately shared with students in each RE classroom. Moreover, accompaniment offers a more sustainable, agile, and targeted mode of supporting the evangelising work of RE teachers working in Catholic secondary schools. Full article

The integration of Lesson Study (LS) and Didactic Suitability Criteria (DSC) presents an innovative framework for enhancing mathematics teacher training in higher education. This study examines how LS-DSC fosters instructional refinement, professional growth, and pedagogical transformation among in-service teachers. Using a quasi-experimental mixed-methods approach, the study analyzed data from 520 mathematics educators participating in a six-month training program incorporating collaborative lesson planning, structured pedagogical assessment, and reflective teaching practices. Findings indicate significant improvements in instructional design, mathematical discourse facilitation, and adaptive teaching strategies, with post-training evaluations demonstrating a strong positive correlation (r = 0.78) between initial competency levels and learning gains. Participants reported increased confidence in implementing student-centered methodologies and sustained engagement in peer collaboration beyond the training period. The results align with prior research emphasizing the effectiveness of lesson study models and structured evaluation frameworks in teacher professionalization. This study contributes to higher education policy and practice by advocating for the institutional adoption of LS-DSC methodologies to promote evidence-based professional development. Future research should explore the long-term scalability of LS-DSC in diverse educational contexts and its impact on student learning outcomes. Full article