In higher education, accreditation is a quality assurance process, where an institution demonstrates a commitment to delivering high quality programs and services to their students. For business schools nationally and internationally the Association to Advance Collegiate Schools of Business (AACSB) accreditation is the gold standard. For a business school to receive and subsequently maintain accreditation, the school must undertake a rigorous, time consuming reporting and peer review process, to demonstrate alignment with the AACSB Standards. For this project we create a hybrid context retrieval augmented generation pipeline that can assist in the documentation alignment and reporting process necessary for accreditation. We implement both a vector database and knowledge graph, as knowledge stores containing both institutional data and AACSB Standard data. The output of the pipeline can be used by institution stakeholders to build their accreditation report, dually grounded by the context from the knowledge stores. To develop our knowledge graphs we utilized both a manual construction process as well as an LLM Augmented Knowledge Graph approach. We evaluated the pipeline using the RAGAs framework and observed optimal performance on answer relevancy and answer correctness metrics.

翻译：在高等教育领域，认证是一种质量保证流程，机构通过该流程展现其致力于为学生提供高质量项目与服务的承诺。对于国内外商学院而言，国际商学院联合会（AACSB）认证被视为黄金标准。商学院为获得并持续保持认证资格，必须经历严格且耗时的报告与同行评审流程，以证明其符合AACSB标准。本项目构建了一种混合上下文检索增强生成流程，能够辅助完成认证所需的文档对齐与报告生成工作。我们同时实现了向量数据库与知识图谱作为知识存储库，其中包含机构数据与AACSB标准数据。该流程的输出可供机构利益相关方用于构建认证报告，其内容通过知识存储库的双重上下文实现可靠溯源。在知识图谱构建过程中，我们采用了人工构建与LLM增强知识图谱相结合的方法。通过RAGAs框架对流程进行评估，结果显示其在答案相关性与答案准确性指标上均达到最优性能。