Embodied AR Language Learning Through Everyday Object Interactions: A Demonstration of EARLL

Learning a new language is an exciting and important yet often challenging goal. To support foreign language acquisition, we introduce EARLL, an embodied and context-aware language learning application for AR glasses. EARLL leverages real-time computer vision and depth sensing to continuously segment and localize objects in users’ surroundings, check for hand-object manipulations, and then subtly trigger foreign vocabulary prompts relevant to that object. In this demo paper, we present our initial EARLL prototype and highlight current challenges and future opportunities with always-available, wearable, embodied AR language learning.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3672539.3686746
Citations
2
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
0.50
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Embodied AR Language Learning Through Everyday Object Interactions: A Demonstration of EARLL

Jon E. Froehlich, S. H. Kim, Jaewook Lee, Minji Park et al.
2 citations
Human Pose and Action Recognition
0.50
article

Embodied AR Language Learning Through Everyday Object Interactions: A Demonstration of EARLL

Jon E. Froehlich, S. H. Kim, Jaewook Lee, Minji Park, Catherine L Rasgaitis
article en
2 citations

Abstract

Learning a new language is an exciting and important yet often challenging goal. To support foreign language acquisition, we introduce EARLL, an embodied and context-aware language learning application for AR glasses. EARLL leverages real-time computer vision and depth sensing to continuously segment and localize objects in users’ surroundings, check for hand-object manipulations, and then subtly trigger foreign vocabulary prompts relevant to that object. In this demo paper, we present our initial EARLL prototype and highlight current challenges and future opportunities with always-available, wearable, embodied AR language learning.

Seoul National University (KR), University of Washington (US), Sungkyunkwan University (KR)
Quality Education
Openalex Percentile: Top 34%
Human Pose and Action Recognition
0.50
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