We present a breath-driven odor display device that enables spatial odor perception in virtual reality, relying on users’ natural inhalation rather than pumps or fans. The device supports rapid concentration adjustment through two models: a continuous gradient (monotonic concentration change with position) and a plume (intermittent, fluctuating patterns resembling natural dispersal). To explore its potential, we conducted a proof-of-concept evaluation across four tasks: concentration discrimination, direction and distance localization, and integrated position searching. Results show that the device can dynamically modulate odor concentration for spatial olfactory perception. Our findings further reveal complementary strengths and limitations of the two models—gradients support stable, precise cues, whereas plumes better emulate natural variability. This work introduces a simple yet effective method for simulating spatial odor experiences in VR, offering a lightweight, energy-efficient pathway that expands the design space for olfactory interaction research in virtual environments.

The Satellite Internet of Things (SatIoT) enables integrated space–air–ground connectivity and information exchange through satellite communication networks and distributed sensors. Its primary advantage lies in achieving global coverage, effectively addressing communication challenges in signal-deprived regions. However, image transmission in SatIoT systems is severely constrained by limited satellite bandwidth, placing high demands on the efficiency and adaptability of compression algorithms. To address these challenges, this work adopts a co-design approach at both the algorithmic and system levels. At the algorithmic level, we implement a deep learning-based image compression framework. To improve the Rate-Distortion performance of the algorithm, 1) we propose a plug-and-play feature extraction module that integrates the strengths of Transformer and CNN to capture both global and local features, preserving critical image details while eliminating redundancy. 2) A Multi Channel Scaling Enhancement module is proposed, which can efficiently fuse features without performance loss and reduce the computational overhead through channel scaling. The experimental results show that they significantly improve the Rate-Distortion performance of mainstream compression algorithms with relatively low computational consumption. At the system level, we design a complete SatIoT communication chain comprising an edge terminal, the Tiantong satellite, and a ground receiving terminal. The optimized compression algorithm is deployed on the edge terminal to enable edge computing, supporting image acquisition, compression, and satellite-based transmission. The system achieves reliable, end-to-end image transmission from signal-deprived regions to ground networks, greatly improving the efficiency and robustness of satellite-based image delivery.

Today, many students learn to speak a foreign language by listening to and repeating pre-recorded materials due to the lack of practice opportunities with human partners. Leveraging recent advancements in AI, Speech, and NLP, we developed EnglishBot, a language learning chatbot that converses with students interactively on college-related topics and provides adaptive feedback. We evaluated EnglishBot against a traditional listen-and-repeat interface with 56 Chinese college students through two six-day user studies under both voluntary and fixed-usage conditions. Students’ fluency improved more with EnglishBot as evaluated by the IELTS grading standard for voluntary learning. EnglishBot users also showed higher engagement and voluntarily spent 2.1 times more time interacting with EnglishBot. Our results suggest that conversational interfaces may benefit foreign learners’ oral language learning, particularly under casual learning settings.

The use of digital examples plays a critical role in mobile UI design. Yet, it remains unclear how UX/UI designers manage (i.e., collect, archive, and utilize) examples to facilitate their design processes at different stages, and what possible challenges are imposed on the design of proper tools to support these practices. In this paper, we conduct a qualitative interview study with mobile UI/UX designers (12 experts and 12 novices), deriving the commonality in practices and analyzing possible differences across four design phases (Discover, Define, Develop, and Deliver) and expertise. In brief, we find that there is more diverse and frequent use of examples in the Discover and Develop phases, and that experts take more diverse advantage of the information from examples compared to novices. We further identify the challenges faced by designers when using existing example management services, and propose potential design implications for the development of more supportive design tools in the future.

A key challenge in education is effectively engaging children in learning activities. We investigated how a narrative story impacts engagement and learning, as well as how feedback can provide further benefits. To do so, we created an interactive, tablet-based learning platform with a multi-step math task designed using Common Core State Standards. Subjects completed a pretest and then were assigned to a condition, either one of three variations of the system (narratives, narratives with hints, and narratives with a tutoring chatbot using wizard-of-oz techniques) or a control system that has children complete the same learning task without narratives nor feedback, before the subjects completed a post test. 72 children in U.S. grades 3--5 participated. Our results showed that embedding learning activities into narratives boosted children's engagement as evaluated by coding video responses and surveys, and the integration of a tutoring chatbot improved learning outcomes on the assessment. These results provide evidence that a narrative-based tutoring system with chatbot-mediated help may support effective learning experiences for children.

Digitization of education has brought a tremendous amount of online materials that are potentially useful for language learners to practice their reading skills. However, these digital materials rarely help with conversational practice, a key component of foreign language learning. Leveraging recent advances in chatbot technologies, we developed BookBuddy, a scalable virtual reading companion that can turn any reading material into an interactive conversation-based English lesson. We piloted our virtual tutor with five 6-year-old native Chinese-speaking children currently learning English. Preliminary results suggest that children enjoyed speaking English with our virtual tutoring chatbot and were highly engaged during the interaction.