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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.07707 |
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| _version_ | 1866912419750608896 |
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| author | Teimouri, Maryam Ginter, Filip Suovuo, Tomi "bgt" |
| author_facet | Teimouri, Maryam Ginter, Filip Suovuo, Tomi "bgt" |
| contents | This paper explores how Mixed Reality (MR) and 2D video conferencing influence children's communication during a gesture-based guessing game. Finnish-speaking participants engaged in a short collaborative task using two different setups: Microsoft HoloLens MR and Zoom. Audio-video recordings were transcribed and analyzed using Large Language Models (LLMs), enabling iterative correction, translation, and annotation. Despite limitations in annotations' accuracy and agreement, automated approaches significantly reduced processing time and allowed non-Finnish-speaking researchers to participate in data analysis. Evaluations highlight both the efficiency and constraints of LLM-based analyses for capturing children's interactions across these platforms. Initial findings indicate that MR fosters richer interaction, evidenced by higher emotional expression during annotation, and heightened engagement, while Zoom offers simplicity and accessibility. This study underscores the potential of MR to enhance collaborative learning experiences for children in distributed settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interaction Analysis by Humans and AI: A Comparative Perspective Teimouri, Maryam Ginter, Filip Suovuo, Tomi "bgt" Human-Computer Interaction This paper explores how Mixed Reality (MR) and 2D video conferencing influence children's communication during a gesture-based guessing game. Finnish-speaking participants engaged in a short collaborative task using two different setups: Microsoft HoloLens MR and Zoom. Audio-video recordings were transcribed and analyzed using Large Language Models (LLMs), enabling iterative correction, translation, and annotation. Despite limitations in annotations' accuracy and agreement, automated approaches significantly reduced processing time and allowed non-Finnish-speaking researchers to participate in data analysis. Evaluations highlight both the efficiency and constraints of LLM-based analyses for capturing children's interactions across these platforms. Initial findings indicate that MR fosters richer interaction, evidenced by higher emotional expression during annotation, and heightened engagement, while Zoom offers simplicity and accessibility. This study underscores the potential of MR to enhance collaborative learning experiences for children in distributed settings. |
| title | Interaction Analysis by Humans and AI: A Comparative Perspective |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.07707 |