AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab
Ebuka Okpala, Nishant Vishwamitra, Keyan Guo, Song Liao, Long Cheng,, Hongxin Hu, Yongkai Wu, Xiaohong Yuan, Jeannette Wade, Sajad Khorsandroo

TL;DR
This paper presents a hands-on lab platform designed to teach non-computing students about AI-based cyberharassment detection, addressing the challenge of interdisciplinary engagement in social cybersecurity education.
Contribution
It introduces a novel experiential learning lab for non-technical students to understand AI and cyberharassment detection, with lessons learned from implementation at North Carolina A&T State University.
Findings
Students' understanding of AI concepts improved
Students gained insights into cyberharassment detection
Moderate increase in students' AI knowledge
Abstract
Cyberharassment is a critical, socially relevant cybersecurity problem because of the adverse effects it can have on targeted groups or individuals. While progress has been made in understanding cyber-harassment, its detection, attacks on artificial intelligence (AI) based cyberharassment systems, and the social problems in cyberharassment detectors, little has been done in designing experiential learning educational materials that engage students in this emerging social cybersecurity in the era of AI. Experiential learning opportunities are usually provided through capstone projects and engineering design courses in STEM programs such as computer science. While capstone projects are an excellent example of experiential learning, given the interdisciplinary nature of this emerging social cybersecurity problem, it can be challenging to use them to engage non-computing students without…
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Taxonomy
TopicsDigital and Cyber Forensics · Information and Cyber Security · Advanced Malware Detection Techniques
MethodsSparse Evolutionary Training
