TY - GEN
T1 - ConversAR
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025
AU - Bendarkawi, Jad
AU - Ponce, Ashley
AU - Mata, Sean Chidozie
AU - Aliu, Aminah
AU - Liu, Yuhan
AU - Zhang, Lei
AU - Liaqat, Amna
AU - Rao, Varun Nagaraj
AU - Monroy-Hernández, Andrés
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/4/26
Y1 - 2025/4/26
N2 - 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.
AB - 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.
KW - Augmented Reality (AR)
KW - Embodied Agents
KW - Language Learning
KW - Large Language Models (LLMs)
KW - Second Language Acquisition
UR - http://www.scopus.com/inward/record.url?scp=105005746128&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105005746128&partnerID=8YFLogxK
U2 - 10.1145/3706599.3720162
DO - 10.1145/3706599.3720162
M3 - Conference contribution
AN - SCOPUS:105005746128
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 26 April 2025 through 1 May 2025
ER -