Social work programs lack systematic methods to align curricula with employer expectations, typically relying on advisory input and alumni surveys rather than direct analysis of workforce requirements. This paper presents a case study demonstrating how one MSW program used artificial intelligence tools to generate organizational intelligence from job posting data for curriculum planning. Using a locally deployed language model, we classified over 40,000 job postings for MSW relevance and alignment with eight practice specializations, then extracted skills, therapeutic modalities, and technology competencies. Interpersonal Practice dominated the employment landscape, followed by Children, Youth, and Families. Clinical Assessment and Case Management emerged as cross-cutting competencies. Macro-level specializations showed co-occurrence patterns among partially aligned positions that largely disappeared among positions requiring MSW credentials specifically. Trauma-informed care appeared in management and evaluation roles, reflecting its expansion from clinical modality to organizational framework. The methodology demonstrates a transferable approach that other programs can adapt for strategic planning, and the findings illustrate the type of intelligence such analysis can yield. The patterns identified entered faculty deliberation as one input among many, interpreted by stakeholders with contextual knowledge no dataset can fully capture.

翻译：社会工作项目缺乏系统的方法来使课程与雇主期望相契合，通常依赖咨询委员会意见和校友调查，而非直接分析劳动力需求。本文展示了一个案例研究，说明一个MSW项目如何利用人工智能工具从职位发布数据中生成组织情报，用于课程规划。通过使用本地部署的语言模型，我们对超过40,000条职位发布进行了MSW相关性及与八种实践专长匹配度的分类，随后提取了技能、治疗模式和技术能力。人际实践在就业领域中占据主导地位，其次是儿童、青少年与家庭方向。临床评估和个案管理成为跨领域核心能力。宏观层面的专长在部分匹配职位中显示出共现模式，而在明确要求MSW资质的职位中，这些模式基本消失。创伤知情照护出现在管理和评估岗位中，反映了其从临床模式向组织框架的扩展。该方法论展示了一种可迁移的路径，其他项目可据此调整用于战略规划，而研究结果则说明了此类分析所能提供的情报类型。所识别的模式作为众多参考因素之一进入教师讨论环节，由具有情境知识的利益相关者进行解读，而任何数据集都无法完全捕捉这些知识。