Teaching American Sign Language in Mixed Reality
Qijia Shao, Amy Sniffen, Julien Blanchet, Megan E. Hillis, Xinyu Shi, Themistoklis K. Haris, Jason Liu, Jason Lamberton, Melissa Malzkuhn, Lorna C. Quandt, James Mahoney, David J. M. Kraemer
Abstract
This paper presents a holistic system to scale up the teaching and learning of vocabulary words of American Sign Language (ASL). The system leverages the most recent mixed-reality technology to allow the user to perceive her own hands in an immersive learning environment with first-and third-person views for motion demonstration and practice. Precise motion sensing is used to record and evaluate motion, providing real-time feedback tailored to the specific learner. As part of this evaluation, learner motions are matched to features derived from the Hamburg Notation System (HNS) developed by sign-language linguists. We develop a prototype to evaluate the efficacy of mixed-reality-based interactive motion teaching. Results with 60 participants show a statistically significant improvement in learning ASL signs when using our system, in comparison to traditional desktop-based, non-interactive learning. We expect this approach to ultimately allow teaching and guided practice of thousands of signs.
CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computer systems organization → Embedded systems.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers3
- Catch Me If You Can: Laser Tethering with Highly Mobile TargetsCharles J. Carver, Hadleigh Schwartz, Qijia Shao, Nicholas Shade et al.NSDI 2024 · 4 citations
- UltraBoard: Always-available Wearable Ultrasonic Mid-air Haptic Interface for Responsive and Robust VR InputsChanghyeon Park, Yubin Lee, Sang Ho YoonUbiComp 2025 · 3 citations
- AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive LearningShi Liu, Martin Feick, Linus Bierhoff, Alexander MaedcheCHI 2026 · 2 citations
Builds on1
Related papers
- Toward Scalable ASL Education: Egocentric Stereo Sensing with LLM Feedback for Error-Aware LearningYongxiang Cai, Zhenghao Li, Taiting Lu, Yanjun Zhu et al.CHI 2026 · 2 citations
- Design and Evaluation of Hybrid Search for American Sign Language to English Dictionaries: Making the Most of Imperfect Sign RecognitionSaad Hassan, Akhter Al Amin, Alexis Gordon, Sooyeon Lee et al.CHI 2022 · 19 citations
- HoloHand: Bidirectional Motion-Language Modeling for Semantic Hand Interaction in Immersive EnvironmentsYingjing Xiao, Wolin Liang, Zhengte Cai, Yang Gao et al.UbiComp 2026
- SignRing: Continuous American Sign Language Recognition Using IMU Rings and Virtual IMU DataJiyang Li, Lin Huang, Siddharth Shah, Sean J. Jones et al.UbiComp 2023 · 27 citations
- SIGMA-ASL: Sensor-Integrated Multimodal Dataset for Sign Language RecognitionXiaofang Xiao, Guangchao Li, Guangrong Zhao, Qi Lin et al.UbiComp 2026
