Intelligent Infrastructure Facilitating Sequence Recommendation for Cybersecurity Education Systems

Abstract

The ability to incorporate original and adapted data into query-based storage structures to provide dynamic and timely service to sequence recommendation systems is a continuous goal of learning management systems. This can be a challenging goal when data integrity and student privacy are paramount. We are developing a hybrid machine learning-assisted system (CyberTaliesin) for cybersecurity educational support. In this poster, we present the early building blocks of the system involving the use of federated knowledge graphs as a trusted knowledge source capable of learning from “less restricted” models such as large language models. How can integrating these tools yield a flexible system that improves sequence recommendations, facilitates concepts such as adaptive and personalized learning, and achieves improved competency-based educational outcomes?