The emergence of innovative technologies has an immense impact on the methods of teaching and learning. With the rapid development of artificial intelligence (AI) technology in recent years, using AI in education has become more and more apparent. This study first outlines the application of AI in the field of education, such as adaptive learning, teaching evaluation, virtual classroom, etc. And then analyses its impact on teaching and learning, which has a positive meaning for improving teachers' teaching levels and students' learning quality. Finally, it puts forward the challenges that AI applications may face in education in the future and provides references for AI to promote education reform.

Background: Skin cancer is a major cause of mortality, and early detection is vital for effective treatment. Diagnosis is challenging because of lesion variability. This study adapts VINCE‐NET, a hybrid deep‐learning model originally designed for stroke detection, to classify melanoma using dermoscopic images. Methods: VINCE‐NET combines vision transformer layers for global context, convolutional neural networks for local features, and long short‐term memory for spatial sequence modeling. During preprocessing, Gaussian blur, normalization, and augmentation were applied to reduce noise and handle class imbalance. During training, the public HAM10000 dataset was used in a central processing unit‐only Google Colab environment (12.72 GB random access memory, 107.7 GB disk) with an AdamW optimizer, a batch size of 12, learning‐rate scheduling, and early stopping (patience = 50). VINCE‐NET's performance was compared with those of a convolutional neural networks, long short‐term memory, residual network with 50 layers (ResNet‐50), visual geometry group network with 16 and 19 layers (VGG‐16/19), and densely connected convolutional network with 121 and 201 layers (DenseNet‐121/201) under identical preprocessing conditions. Results: VINCE‐NET achieved 94.1% accuracy, 95.5% precision, 90.4% recall, a 92.9% F1‐score, and an area under the receiver operating characteristic curve of 0.98 at a training time of 34,308.42 s. Benchmarks showed that VINCE‐NET outperformed baselines while being computationally efficient. Conclusion: VINCE‐NET provides competitive performance for melanoma classification and feasibility in resource‐limited settings. Although promising, VINCE‐NET has not been clinically validated yet. Future work will address resolution, ablation studies, interpretability, and external validation.

Stroke detection using artificial intelligence (AI) continues to show promise in advancing medical imaging diagnostics, particularly in improving accuracy

This study investigates the long-term sustainability of mobile learning (M-learning) integration at the University of Education, Winneba, Ghana. It explores critical factors such as infrastructure, resource allocation, and institutional support. Employing a quantitative research approach, the study utilised a structured questionnaire to collect data from 69 respondents, including Level 400 students from the Information and Communication Technology and Communication and Media Studies departments. Key findings highlight the importance of robust infrastructure, adequate resource distribution, and comprehensive institutional backing for sustainable M-learning practices. The study offers valuable insights for policymakers, educators, and institutions aiming to enhance the longevity and effectiveness of M-learning initiatives in higher education.