UbiLearn: Supporting English-as-a-Foreign-Language Learners in Reflecting on Conversations Using a Smartwatch MHCI033

What new opportunities can the current ubiquitous computing and AI technologies provide to support English-as-a-Foreign-Language (EFL) learners? To answer the question, we began with a formative study with EFL learners, uncovering multiple challenges during conversations with others and their desire to review such scenes later. We implemented a smartwatch prototype, UbiLearn , which features hand gesture recognition for in-situ multi-context annotation to save moments when learners face difficulty. The annotation is used to generate personalized educational material powered by speech and natural language processing. Through a series of studies, we demonstrated the feasibility and preferred usability of UbiLearn, leading to learners’ enhanced learning satisfaction. Moreover, the annotation data promoted the role of instructors by enabling the tracking of learners’ in-situ proficiency outside their tutoring sessions. We conclude by highlighting emerging opportunities for learners enabled by mobile and AI technologies, along with key considerations.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-09-09
DOI
https://doi.org/10.1145/3743735
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

UbiLearn: Supporting English-as-a-Foreign-Language Learners in Reflecting on Conversations Using a Smartwatch MHCI033

Manami Nakagawa, Riku Arakawa, Hiromu Yakura
Proceedings of the ACM on Human-Computer Interaction
Hand Gesture Recognition Systems
article

UbiLearn: Supporting English-as-a-Foreign-Language Learners in Reflecting on Conversations Using a Smartwatch MHCI033

Manami Nakagawa, Riku Arakawa, Hiromu Yakura
article en

Abstract

What new opportunities can the current ubiquitous computing and AI technologies provide to support English-as-a-Foreign-Language (EFL) learners? To answer the question, we began with a formative study with EFL learners, uncovering multiple challenges during conversations with others and their desire to review such scenes later. We implemented a smartwatch prototype, UbiLearn , which features hand gesture recognition for in-situ multi-context annotation to save moments when learners face difficulty. The annotation is used to generate personalized educational material powered by speech and natural language processing. Through a series of studies, we demonstrated the feasibility and preferred usability of UbiLearn, leading to learners’ enhanced learning satisfaction. Moreover, the annotation data promoted the role of instructors by enabling the tracking of learners’ in-situ proficiency outside their tutoring sessions. We conclude by highlighting emerging opportunities for learners enabled by mobile and AI technologies, along with key considerations.

Proceedings of the ACM on Human-Computer InteractionVol. 9(5)
Max Planck Institute for Human Development (DE), Human Computer Interaction (Switzerland) (CH), Intelligent Machines (Sweden) (SE), Carnegie Mellon University (US), University of California, Davis (US)
Openalex Percentile: Top 76%
Hand Gesture Recognition Systems
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UbiLearn: Supporting English-as-a-Foreign-Language Learners in Reflecting on Conversations Using a Smartwatch MHCI033 — Manami Nakagawa, Riku Arakawa, et al. · Proceedings of the ACM on Human-Computer Interaction (2025) | TGRS Research Map | TGRS