High-density school design has become an important challenge in rapidly urbanizing cities, where land scarcity, increasing educational demand, and evolving pedagogical models generate multiple and interrelated design constraints. Although a wealth of design experience has accumulated through the execution of numerous school planning projects, this knowledge remains fragmented across architectural publications and project narratives. Existing studies have primarily focused on evaluating built environments or individual cases, and limited attention has been paid to how dispersed architectural design reasoning can be systematically extracted, organized, and represented. This study was conducted to explore how AI-assisted semantic modeling can support the extraction and organization of architectural design knowledge from large-scale design discourse through a human–AI collaborative interpretation framework. Using a corpus of 330 documents reporting school design in Shenzhen published between 2017 and 2024, the proposed framework integrates BERTopic-based semantic modeling, scenario–strategy coding, network analysis, and document-based architectural interpretation to establish a continuous workflow from architectural discourse to structured knowledge representation and spatial interpretation. The results reveal a density-conditioned knowledge structure consisting of six interconnected design agendas, 25 recurrent design scenarios, 44 original design strategies, 17 core strategies, four strategy clusters, and four document-supported spatial response patterns. The findings demonstrate that high-density school design knowledge is organized through recurring problem–strategy relationships rather than isolated project solutions. Through human–AI collaborative interpretation, fragmented design narratives are transformed into hierarchical representations linking design concerns, scenarios, strategies, and spatial organizations. The aim of the proposed framework is not to automate architectural decision-making or generate implementation-ready design solutions but to provide a methodological foundation for AI-assisted architectural knowledge retrieval, knowledge representation, and future multimodal design intelligence systems.