Artificial intelligence, as a strategic technology leading the new round of technological revolution and industrial transformation, is profoundly influencing the development direction of education. Professor Xiong Zhang is an important promoter in the field of artificial intelligence education in China, and has long been committed to the research of educational informatization and the improvement of students' artificial intelligence literacy. In recent years, he has put forward a series of forward-looking views on artificial intelligence education and the construction of a learning society. During the 2025 National Higher Education Physics Basic Course Education Academic Seminar, we conducted an exclusive interview with Professor Xiong. Based on the interview content, this paper systematically presents Professor Xiong's main views on artificial intelligence education. Professor Xiong pointed out that artificial intelligence is forcing education reform. The future talent demand will be dominated by enterprises and society, and higher education institutions urgently need to transform. We should seize the catalytic, empowering and guiding role of artificial intelligence to effectively promote the construction of a learning society and truly implement the development of quality education.

Light field (LF) cameras record both intensity and directions of light rays in a scene with a single exposure. Due to the trade-off between spatial and angular dimensions, the spatial resolution of LF images is limited, so super-resolution is widely studied. Pixels follow linear coordinate projection across views in LF images. Hence, auxiliary views nearer to the target view are generally more effective for use in super-resolution. In this paper, an LF-pyramid is proposed based on an angular-distance constraint for discriminatively exploiting auxiliary views. From views of different layers in an LF-pyramid, complementary features of different effectiveness can be extracted. However, shapes of LF-pyramids change for target views with different angular positions. To fully exploit an LF-pyramid, we introduce a pyramid-angular-constraint network for LF super-resolution (LF-PACNet). Specifically, to handle an arbitrary number of views in each layer, an intra-pyramid-layer feature extraction module is designed, which treats all views in the same layer equally in complementary information extraction. Then, to deal with an arbitrary number of layers, a recurrent cross-pyramid-layer feature complementation module is constructed, which discriminatively complements the target view with high-frequency details. Extensive experiments on public datasets demonstrate state-of-the-art performance for our method, both visually and numerically, especially for datasets with large disparities.

The task of detecting three-dimensional objects using only RGB images presents a considerable challenge within the domain of computer vision. The core issue lies in accurately performing epipolar geometry matching between multiple views to obtain latent geometric priors. Existing methods establish correspondences along epipolar line features in voxel space through various layers of convolution. However, this step often occurs in the later stages of the network, which limits overall performance. To address this challenge, we introduce a novel framework, ImVoxelENet, that integrates a geometric epipolar constraint. We start from the back-projection of pixel-wise features and design an attention mechanism that captures the relationship between forward and backward features along the ray for multiple views. This approach enables the early establishment of geometric correspondences and structural connections between epipolar lines. Using ScanNetV2 as a benchmark, extensive comparative and ablation experiments demonstrate that our proposed network achieves a 1.1% improvement in mAP, highlighting its effectiveness in enhancing 3D object detection performance. Our code is available at https://github.com/xug-coder/ImVoxelENet.

Light field (LF) cameras record multiple perspectives by a sparse sampling of real scenes, and these perspectives provide complementary information. This information is beneficial to LF super-resolution (LFSR). Compared with traditional single-imagesuper-resolution, LF can exploit parallax structure and perspective correlation among different LF views. Furthermore, the performance of existing methods are limited as they fail to deeply explore the complementary information across LF views. In this paper, we propose a novel network, called the light field complementary-view feature attention network (LF-CFANet), to improve LFSR by dynamically learning the complementary information in LF views. Specifically, we design a residual complementary-view spatial and channel attention module (RCSCAM) to effectively interact with complementary information between complementary views. Moreover, RCSCAM captures the relationships between different channels, and it is able to generate informative features for reconstructing LF images while ignoring redundant information. Then, a maximum-difference information supplementary branch (MDISB) is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF images. This branch also can guide the process of reconstruction. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method. The proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences. This paper introduces a learning style model to represent features of online learners. It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style (AROLS), which implements learning resource adaptation by mining learners’ behavioral data. First, AROLS creates learner clusters according to their online learning styles. Second, it applies Collaborative Filtering (CF) and association rule mining to extract the preferences and behavioral patterns of each cluster. Finally, it generates a personalized recommendation set of variable size. A real-world dataset is employed for some experiments. Results show that our online learning style model is conducive to the learners’ data mining, and AROLS evidently outperforms the traditional CF method.