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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/2509.11600 |
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| _version_ | 1866914037907849216 |
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| author | Lin, Lin Wu, Ming Ren, Anyu Wu, Zhanwei Gong, Daojun Xiao, Ruowei |
| author_facet | Lin, Lin Wu, Ming Ren, Anyu Wu, Zhanwei Gong, Daojun Xiao, Ruowei |
| contents | In virtual or hybrid co-present events, biodata is emerging as a new paradigm of social cues. While it is able to reveal individuals' inner states, the technology-mediated representation of biodata in social contexts remains underexplored. This study aims to uncover human cognitive preferences and patterns for biodata expression and leverage this knowledge to guide generative AI (GenAI) in creating biodata representations for co-present experiences, aligning with the broader concept of Human-in-the-loop. We conducted a user elicitation workshop with 30 HCI experts and investigated the results using qualitative analysis. Based on our findings, we further propose a GenAI-driven framework: BioMetaphor. Our framework demonstration shows that current GenAI can learn and express visual biodata cues in an event-adpated, human-like manner. This human-centered approach engages users in research, revealing the underlying cognition constructions for biodata expression while demonstrating how such knowledge can inform the design and development of future empathic technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioMetaphor: AI-Generated Biodata Representations for Virtual Co-Present Events Lin, Lin Wu, Ming Ren, Anyu Wu, Zhanwei Gong, Daojun Xiao, Ruowei Human-Computer Interaction In virtual or hybrid co-present events, biodata is emerging as a new paradigm of social cues. While it is able to reveal individuals' inner states, the technology-mediated representation of biodata in social contexts remains underexplored. This study aims to uncover human cognitive preferences and patterns for biodata expression and leverage this knowledge to guide generative AI (GenAI) in creating biodata representations for co-present experiences, aligning with the broader concept of Human-in-the-loop. We conducted a user elicitation workshop with 30 HCI experts and investigated the results using qualitative analysis. Based on our findings, we further propose a GenAI-driven framework: BioMetaphor. Our framework demonstration shows that current GenAI can learn and express visual biodata cues in an event-adpated, human-like manner. This human-centered approach engages users in research, revealing the underlying cognition constructions for biodata expression while demonstrating how such knowledge can inform the design and development of future empathic technologies. |
| title | BioMetaphor: AI-Generated Biodata Representations for Virtual Co-Present Events |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.11600 |