NSF Award
2422643
The broader impact of this I-Corps project is the development of a personalized cybersecurity awareness and training platform, designed to address the limitations of traditional cybersecurity training by developing artificial intelligence (AI) algorithms and utilizing a Social Learning Based Software as a Service model. This platform leverages AI algorithms to tailor the learning experience to each user's level of expertise and educational background, based on a differentiated instruction methodology. This solution ensures that the content is both relevant and appropriately challenging as users progress. The AI system also provides real-time feedback, allowing users to learn from mistakes and gradually improve their cybersecurity skills. The commercial potential of this platform lies in the increasing demand for cybersecurity skills in the workforce and the need for ongoing education to combat emerging cyber threats.<br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of machine learning algorithms to support a personalized learning platform tailored to each user's skill level and educational background. The approach leverages differentiated instruction methodologies, ensuring the training content adapts to be both relevant and appropriately challenging as participants progress. A key feature of this AI-driven system is its capability to provide real-time feedback, enabling learners to promptly address mistakes and incrementally enhance their cybersecurity skills. This innovation builds upon foundational research that explored different ways to implement cybersecurity in a particular application scenario, extending the application of AI in cybersecurity educational contexts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
