Virtual reality (VR) gaming offers uniquely immersive experiences but poses challenges due to its complexity and novelty. This study explores how both hedonic (e.g., immersion, presence) and pragmatic (e.g., discomfort, control) user experience (UX) dimensions affect user satisfaction. Using over 350,000 Steam.com reviews, we developed a new sentiment analysis method combining zero-shot classification and a fine-tuned RoBERTa model. Results show all UX dimensions are positively linked to satisfaction, but their impact varies by gaming experience of users. General gaming experience strengthens the effect of hedonic dimensions, while game-specific experience diminishes the effect of all UX dimensions. Moreover, users with general experience mention all UX dimensions more frequently, whereas those with game-specific experience emphasize hedonic over pragmatic aspects. These findings reveal how different types of gaming experience shape the perception and evaluation of VR UX.

In today’s academic scenario, understanding learners’ emotional responses during academic learning is important to improve learning ability. This study provides a comparative analysis of facial emotions from interactions with both real human (RH) and virtual human (VH) in the context of online academic learning. Facial video data were collected from participants engaged in both RH and VH learning sessions. Facial landmarks were extracted using the MediaPipe Face Mesh model and six emotional states were mapped from computed action unit (AU) scores. A convolutional neural network (CNN) was trained on the FER-2013 and extended CK+ datasets to classify six facial emotional states from the acquired dataset. Emotion intensity was computed based on AU scores for each detected state. Results revealed that happiness and surprise intensities were significantly higher during VH interactions compared to RH. An ANOVA test confirmed statistically significant differences in emotional intensity between RH and VH interactions.

Personal intelligent agents (IAs) are increasingly embedded in everyday life, a trend accelerated by generative AI technologies. Despite their growing presence, these agents often remain fragmented across different life domains and environments. This workshop explores how to design integrated IA ecosystems emphasizing continuity, coordination, and human-centered values. Participants with varied perspectives will collaboratively develop frameworks, scenarios, and guidelines for cohesive personal agent systems that enrich user experiences holistically. By examining factors that shape users' preferences for information integration or separation, we aim to inform the design of coherent, user-aligned multi-agent systems.

Artificial intelligence-powered coding assistants (AI-CAs) have become essential tools in programming; however, there is limited understanding of the mechanisms driving programmers’ adoption of these tools in their daily coding tasks. This study aims to examine the role of utilitarian and hedonic values in the adoption of AI-CAs by extending the Technology Acceptance Model (TAM). The data gathered from an online survey of 283 Korean programmers is analyzed using structural equation modeling. The results showed that both perceived enjoyment and perceived usefulness positively influence the attitudes and usage intentions toward AI-CAs. Interestingly, perceived enjoyment has a stronger influence on the intention to use than perceived usefulness, suggesting that recognizing the intrinsic motivation for using AI-CAs is crucial for fully leveraging their benefits. The model also confirms that the compatibility and relative advantages of AI-CAs enhance their adoption. This research enriches the current knowledge base by incorporating hedonic values into the TAM, offering new insights on the design of AI-CAs aimed at enhancing their adoption among developers.

Users often select different modalities in multimodal systems based on context and function. This study aimed to investigate the impact of context on users’ modality selection in a multimodal system. A modality selection task was presented through the developed multimodal system, where the task’s difficulty was controlled based on the number of syllables and touches. The experiments were examined in four usage contexts (baseline, watching, reading, and driving), and the selected modality, physical and mental effort, and satisfaction were measured. A regression model for predicting modality selection was established using binomial logistic regression. The results showed that voice usage increased in the order of baseline, watching, reading, and driving contexts. It was revealed that these changes were attributable to differences in physical and mental interaction efforts of modalities according to contexts. Our finding provides valuable insights into users’ modality selection in different contexts, which could inform the design of more efficient multimodal systems.

The purpose of this study was to explore the unit of information and the size of the unit for designing a voice user interface. Through two experiments, this study investigated what form the information (the unit of information) should take and what size of that (the size of unit) should be when people were provided information by voice interfaces. Participants were presented with a task to recall (OX quiz) by listening to and remembering information (based on an encyclopedia) provided by smart speakers. In Experiment 1, it was revealed that participants stored information in their memory span on a sentence-by-sentences basis to determine how much information they could remember. In Experiment 2, sentence-based information was presented in various sizes, and participants evaluated 17 information units consisting of up to nine words as their memory limit. This information unit-based voice interface design could help improve users’ memory performance and usability.

This study investigated the effects of the target size and movement distance on user performance and workload in a virtual reality (VR) environment. In a repeated-measures laboratory study, 36 participants (18 male and 18 female) performed the drag-and-drop task as a standard human–computer interaction (HCI) task with different target sizes (1, 1.5, 2, 2.5, and 3 cm) and movement distances (5, 9, 13, 17, and 20 cm). Task completion time (TCT), error rate, and movement time (MT) were measured as performance indices, whereas physical load and effort were assessed as workload indices. The results demonstrated that the target size and movement distance significantly affected all performance measures and workload indices. Large target sizes produced better performance and lower workloads; however, large movement distances decreased performance and increased workload. However, sex had no significant effect on the performance or workload during the drag-and-drop tasks. The best target sizes were 2.5 and 3 cm, and the worst size was 1 cm. The best movement distances were 5 and 9 cm, and the worst distance was 20 cm. The results of this study can provide useful reference information for developing VR technology based on human factors and demonstrate that additional basic research is required to reflect the distinctive features of VR in the future.

The objective of this study was to investigate the touch area that can be comfortably reached by the thumb during one-handed smartphone interaction. To achieve the research objective, we introduced the concept of natural thumb position when designing a tapping task and conducted an user experiment. The independent variables were the target distance and direction from the natural thumb position, and the three dependent variables were the task performance, information throughput, and touch accuracy. The results showed that participants performed the task comfortably in the diagonal direction between the upper right and the lower left side of the screen. The task performance deteriorated as the target distance increased, especially at 45 mm or more. The touch accuracy was measured using X- and Y-coordinates data. Participants touched the left side of the target center, except near the proximal area of the hand. They also touched the points above the center of the target in the upper screen area and points below the center of the target in the lower screen area. The findings of this study provided insights for designing a smartphone touch interface considering the comfortable touch areas of one-handed interaction.