Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation Processes
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866910007169122304 |
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| author | Quan, Kexin Albassam, Dina Wu, Mengke Ding, Zijian Chin, Jessie |
| author_facet | Quan, Kexin Albassam, Dina Wu, Mengke Ding, Zijian Chin, Jessie |
| contents | Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23904 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation Processes Quan, Kexin Albassam, Dina Wu, Mengke Ding, Zijian Chin, Jessie Human-Computer Interaction Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas. |
| title | Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation Processes |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.23904 |