Intelligent Infrastructure Facilitating Sequence Recommendation for Cybersecurity Education Systems




federated knowledge graphs, large language models, cybersecurity


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?

Author Biography

Douglas A. Talbert, Tennessee Tech University

Dr. Douglas Talbert is a full professor in the Computer Science Department at Tennessee Tech University, serving as Associate Chair.  He is also the Interim Director for the Cybersecurity Education, Research, and Outreach Center and Co-Director for the Machine Intelligence and Data Science (MInDS) Center.




How to Cite

Brown, E., & Talbert, D. (2024). Intelligent Infrastructure Facilitating Sequence Recommendation for Cybersecurity Education Systems. The International FLAIRS Conference Proceedings, 37(1).