Generative AI is reshaping education, but it also raises concerns about instability and overreliance. In programming classrooms, we aim to leverage its feedback capabilities while reinforcing the educator’s role in guiding student–AI interactions. We developed ClassAid, a real-time orchestration system that integrates TA Agents to provide personalized support and an AI-driven dashboard that visualizes student–AI interactions, enabling instructors to dynamically adjust TA Agent modes. Instructors can configure the Agent to provide technical feedback (direct coding solutions), heuristic feedback (hint-based guidance), automatic feedback (autonomously selecting technical or heuristic support), or silent operation (no AI support). We evaluated ClassAid through three aspects: (1) the TA Agents’ performance, (2) feedback from 54 students and one instructor during a classroom deployment, and (3) interviews with eight educators. Results demonstrate that dynamic instructor control over AI supports effective real-time personalized feedback and provides design implications for integrating AI into authentic educational settings.

Humans encounter a vast array of sensory stimuli in their everyday lives. However, many visualization techniques primarily utilize visual feedback, which may disregard certain intricate details. Relying on a single visual channel may overlook complex layouts. However, how haptic force feedback can be used to assist visualization remained under-explored. In this work, we initially conducted a literature review to identify potential problems in the visualization of large datasets and engaged in discussions with domain experts to explore the potential of haptic force feedback and visual collision representation. Subsequently, we designed an innovative haptic force feedback knob, which included 3 primary modules and 29 elements. To evaluate the clarity and usefulness of this design space, we conducted a workshop and devised “recommended solutions” for the identified visualization problems. Finally, we implemented a prototype of the haptic force feedback knob and assessed its performance on scatterplot and parallel coordinate plot tasks using large datasets. The results indicated that the knob prototype could reduce visual strain and enhance the efficiency of visualization tasks.

In the complex landscape of the Internet, inter-domain routing systems are essential for ensuring seamless connectivity and reachability across autonomous systems. However, the lack of dependable security validation mechanisms in these systems poses persistent challenges. Vulnerabilities such as prefix hijacking, path forgery, and route leakage not only compromise network operators and users, but also threaten the stability and accessibility of the Internet’s core infrastructure. To address this, visualization and visual analytics techniques are adept at identifying and detecting security threats, offering network administrators effective methods to monitor and maintain network operations. This paper presents a comprehensive survey of the state-of-the-art research in visualization and visual analytics for inter-domain routing security. We delineate four scenarios for tasks analysis in network visualization: monitoring, detection, verification, and discovery. Each category is explored in detail, focusing on the employed data sources and visualization techniques. Several key findings are presented at the end of each category, aimed at providing researchers and practitioners with research inspiration. Furthermore, we examine the trends of academic interest observed in recent decades and propose potential directions for future research in visual analytics pertaining to Internet infrastructure security.

Video data refers to digital information in the form of a series of frames or images representing continuous motion captured by a video recording device. In various domains such as security, sports, education, and entertainment, a significant amount of video data is generated and stored daily. However, analyzing these videos manually is challenging due to their intrinsic characteristics, including large-scale, redundancy, contextual dependencies, and multimodality. Consequently, researchers have extensively explored visualization techniques to address these complexities. In this investigation, we review the state-of-the-art techniques in video visualization and visual analysis. Initially, we provide an overview of the design space for video visualization and visual analysis techniques. Subsequently, we organize and classify these techniques based on visual analysis tasks and application scenarios, providing detailed descriptions within each category. Drawing upon a comprehensive review of existing research, we provide a critical evaluation and propose potential opportunities for future research. Additionally, we have developed a web-based survey browser for convenient exploration of our created classification framework and the associated scholarly articles (https://zjutvis.github.io/VOVideo/).

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.

With the popularity of online learning in recent decades, MOOCs (Massive Open Online Courses) are increasingly pervasive and widely used in many areas. Visualizing online learning is particularly important because it helps to analyze learner performance, evaluate the effectiveness of online learning platforms, and predict dropout risks. Due to the large-scale, high-dimensional, and heterogeneous characteristics of the data obtained from online learning, it is difficult to find hidden information. In this paper, we review and classify the existing literature for online learning to better understand the role of visualization in online learning. Our taxonomy is based on four categorizations of online learning tasks: behavior analysis, behavior prediction, learning pattern exploration and assisted learning. Based on our review of relevant literature over the past decade, we also identify several remaining research challenges and future research work.