Yiyin Gu

This paper is an extended version of the best paper from the HUCAPP 2025 conference. The abstract nature of Artificial Intelligence (AI) concepts presents an educational challenge. This paper compares an immersive extended reality (XR) environment to a traditional desktop setup for teaching Neural Networks and Handwritten Digit Recognition. We analyzed differences in student engagement, user experience, and learning outcomes between the two modalities. In this comparative study, 56 participants learned about AI concepts using either an XR headset (Meta Quest 3) or a desktop computer. We collected data on usability, satisfaction, immersion, and likelihood to recommend using the System Usability Scale (SUS), User Satisfaction Questionnaire (USQ), Immersion Presence Questionnaire (IPQ), and Net Promoter Score (NPS) questionnaires. Learning outcomes were assessed via multiple-choice questions administered during the lesson. The XR group reported significantly higher engagement, immersion, satisfaction, and likelihood to recommend the system. However, this increased engagement did not translate to superior learning outcomes; performance on in-lesson questions was comparable between the two groups. XR users also identified challenges, including physical discomfort and unfamiliarity with the technology. The findings suggest that XR can increase student motivation in AI education, but this does not automatically lead to better learning performance. Usability challenges and the novelty of the technology may hinder knowledge absorption. Future work should focus on scaffolding strategies to mitigate these issues and better leverage XR’s educational potential by personalizing the learning experience.

This study examines the use of extended reality (XR) in helping students with conceptual comprehension of artificial intelligence (AI) concepts, specifically neural networks (NNs) and handwritten digit recognition. Using a multi‐methods approach, this study assesses student performance and understanding of such concepts. Student participants ( N = 29) engaged in an XR environment designed to teach NNs and completed in‐lesson assessments consisting of multiple‐choice questions and open‐ended questions. Quantitative data were analyzed using the k ‐means clustering method to classify performance levels based on the accuracy of the answers. The elbow approach determined the number of clusters, and the average silhouette score showed the cluster quality after clustering. Qualitative data underwent thematic analysis to identify challenges in handwritten digit recognition. Results showed that the accuracy of the students' responses ranged from 17% to 100% and could be classified into three groups, and that factors like handwriting clarity, digit placement, and writing style significantly impacted the accuracy of handwritten digit recognition. The findings suggest the potential of using XR for supporting learning and engagement in studying AI concepts. Future research is encouraged to apply XR across various education levels and explore broader AI concepts. This study contributes to the literature on applying XR in computer science education by providing insights into how XR can enhance conceptual comprehension of complex AI concepts like NNs.

The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively.

In this paper, we demonstrate the Information Interactions in Virtual Reality (IIVR) system designed and implemented to study how users interact with abstract information objects in immersive virtual environments in the context of information retrieval. Virtual reality displays are quickly growing as social and personal computing media, and understanding user interactions in these immersive environments is imperative. As a step towards effective information retrieval in such emerging platforms, our system is central to upcoming studies to observe how users engage in information triaging tasks in Virtual Reality (VR). In these studies, we will observe the effects of (1) information layouts and (2) types of interactions in VR. We believe this early system motivates researchers in understanding and designing meaningful interactions for future VR information retrieval applications.