ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909628259893248 |
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| author | Bendarkawi, Jad Ponce, Ashley Mata, Sean Aliu, Aminah Liu, Yuhan Zhang, Lei Liaqat, Amna Rao, Varun Nagaraj Monroy-Hernández, Andrés |
| author_facet | Bendarkawi, Jad Ponce, Ashley Mata, Sean Aliu, Aminah Liu, Yuhan Zhang, Lei Liaqat, Amna Rao, Varun Nagaraj Monroy-Hernández, Andrés |
| contents | Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners Bendarkawi, Jad Ponce, Ashley Mata, Sean Aliu, Aminah Liu, Yuhan Zhang, Lei Liaqat, Amna Rao, Varun Nagaraj Monroy-Hernández, Andrés Human-Computer Interaction Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. |
| title | ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.24000 |