More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs
This work addresses the complex problem of human-AI alignment for families and professionals in early developmental contexts, highlighting the need for a community-governed approach rather than individual optimization.
This paper investigates human-AI alignment in early developmental communities, specifically in parent-child interaction analysis involving families and speech-language pathologists (SLPs). The study, involving five families and three SLPs, traces the flow of MLLM-generated outputs from expert analysis to parent feedback, revealing how authority, responsibility, and emotional risk are distributed among stakeholders.
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.