Using Generative Artificial Intelligence (GAI) in education has opened new avenues for innovation, yet its role as an interactive tool with learners remains underexplored. Research in this domain faces challenges from pedagogical complexities and the variability of AI tools. To address these gaps, this study developed an online project-based learning (PBL) platform incorporating a GAI plug-in and conducted a year-long experiment to analyze its impact. Three sets of experimental analyses were performed to examine learners’ methods, cognitive processes, and learning effectiveness. The findings reveal that GAI significantly influenced students’ learning approaches, cognitive engagement, and learning effectiveness. Additionally, the study demonstrates that PBL offers an effective framework for investigating the educational implications of GAI, providing new insights for future research in this evolving field.

As generative artificial intelligence (GAI) becomes increasingly embedded in academic work, concerns have emerged that students experiencing higher levels

Maleic anhydride (MA) is introduced to fabricate poly(vinylidene fluoride)/expanded graphite (PVDF/EG) composites via one-step melt mixing. SEM micrographs and WAXD results have demonstrated that the addition of MA helps to exfoliate and disperse the EG well in the PVDF matrix by promoting the mobility of PVDF molecular chains and enhancing the interfacial adhesion between the EG layers and the PVDF. Thus, much higher thermal conductivities are obtained for the PVDF/MA/EG composites compared to the PVDF/EG composites that are lacking MA. For instance, The PVDF/MA/EG composite prepared with a mass ratio of 93:14:7 exhibits a high thermal conductivity of up to 0.73 W/mK. It is 32.7% higher than the thermal conductivity of the PVDF/EG composite that is prepared with a mass ratio of 93:7. Moreover, the introduction of MA leads to an increased melting peak temperature and crystallinity due to an increased nucleation site provided by the uniformly dispersed EG in the PVDF matrix. This study provides an efficient preparation method for PVDF/EG composites with a high thermal conductivity.

This study integrates knowledge sharing into traditional teacher training to enhance teachers’ professional development. It addresses the research gap in implementing such integration by developing a video cloud platform that supports online training and knowledge-sharing among teachers. The proposed approach combines the SECI model (socialization, externalization, combination, and internalization) with the video cloud platform to create a new model of teacher e-learning, fostering collaborative learning among teachers in a remote and interactive context. To demonstrate the effectiveness of this approach, the study conducted experiments with Chinese primary and secondary school teachers. The new model was applied in an online professional competence-oriented supporting environment. The results showed a significant improvement in organization and management skills, and a moderate increase in technical literacy, learning, and development capabilities among the participating teachers. These findings have important implications for professional development of teachers, particularly in the context of the ongoing epidemic and the shift towards a networked and intelligent world. By integrating knowledge sharing into traditional training, teachers can enhance professional competence and adapt to the changing educational landscape. The video cloud platform is a valuable tool for facilitating collaborative learning and knowledge creation among teachers.

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.