More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912951315726336 |
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| author | Shi, Weiyan Choo, Kenny Tsu Wei |
| author_facet | Shi, Weiyan Choo, Kenny Tsu Wei |
| contents | In early developmental contexts, particularly in parent-child interaction analysis, alignment involves families and professionals such as speech-language pathologists (SLPs) who interpret children's everyday interactions from different roles. When multimodal large language models (MLLMs) are introduced to support this process, alignment becomes a question of how authority, responsibility, and emotional risk are distributed across stakeholders. Through a three-part study with five families and three SLPs, we trace how MLLM-generated outputs move from expert-facing analysis to parent-facing feedback. We propose layered community alignment: grounding representations in expert-aligned structures, mediating translation through professional guardrails, and enabling family-level adaptation within those boundaries. We argue that alignment in developmental settings should be treated as a community-governed process rather than an individual optimisation problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07134 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs Shi, Weiyan Choo, Kenny Tsu Wei Human-Computer Interaction In early developmental contexts, particularly in parent-child interaction analysis, alignment involves families and professionals such as speech-language pathologists (SLPs) who interpret children's everyday interactions from different roles. When multimodal large language models (MLLMs) are introduced to support this process, alignment becomes a question of how authority, responsibility, and emotional risk are distributed across stakeholders. Through a three-part study with five families and three SLPs, we trace how MLLM-generated outputs move from expert-facing analysis to parent-facing feedback. We propose layered community alignment: grounding representations in expert-aligned structures, mediating translation through professional guardrails, and enabling family-level adaptation within those boundaries. We argue that alignment in developmental settings should be treated as a community-governed process rather than an individual optimisation problem. |
| title | More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.07134 |