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.

翻译：在早期发展情境中，特别是亲子互动分析领域，对齐过程涉及家庭以及言语语言病理学家（SLPs）等专业人员，他们从不同角色解读儿童的日常互动。当引入多模态大语言模型（MLLMs）以支持这一过程时，对齐问题便转化为权威、责任与情感风险如何在各利益相关方之间分配的问题。通过对五个家庭和三位言语语言病理学家开展的三阶段研究，我们追踪了MLLM生成的结果如何从面向专家的分析转化为面向家长的反馈。我们提出分层社群对齐框架：将表征建立在专家对齐的结构基础上，通过专业防护机制进行转译中介，并允许家庭在既定边界内进行适应性调整。我们认为，发展场景中的对齐应被视为社群治理的过程，而非个体优化问题。