ANNOUNCEMENT

NOBEL PRIZE 2026

This year's laureates, their fields, and the discoveries that earned them the prize.Live nowSee the winners
Moonbase
← Back to Awards
NSFI-Corps

I-Corps: Translation Potential of an Artificial Intelligence-Assisted Social Learning Platform for Routinizing Cybersecurity Awareness in Workforce Development

Completed
Institution
Florida International University, FL
Principal investigator
Xueping Liang
Funding
$50K
Duration
2024–2025
Open portfolio

Moonbase score

Still being scored

Abstract

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. 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. 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.

LOADING MOONBASE SCORE

I-CorpsDirectorate for Technology, Innovation, and PartnershipsADVANCED LEARNING TECHNOLOGIESplatformcybersecuritytranslation potentiallearning platformappropriatelymistakesdifferentiatedinstruction

Questions for Florida International University

Answered by the team behind this research.

Same institution, same topic, scored first.