AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab

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 cyberharassment, 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 prior knowledge of AI. Because of this, we were motivated to develop a hands-on lab platform that provided experiential learning experiences to non-computing students with little or no background knowledge in AI. We also discussed the lessons learned in developing this lab for non-computing students. In this lab used by social science students at North Carolina A&T University across two semesters (spring and fall) in 2022, students are given a detailed lab manual and are to complete a set of well-detailed tasks. Through this process, students learn AI concepts and the application of AI for cyberharassment detection. Using pre-and post-surveys, we asked students to rate their knowledge or skills in AI and their understanding of the concepts learned. The results revealed that the students moderately understood the concepts of AI and cyberharassment.

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Publication Details

Journal
Journal of The Colloquium for Information Systems Security Education
Published
2025-04-20
DOI
https://doi.org/10.53735/cisse.v12i1.194
Citations
1
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
3.25
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article

AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab

Song Liao, Jeannette Wade, Keyan Guo, Hongxin Hu et al.
1 citations
Journal of The Colloquium for Information Systems Security Education
Digital and Cyber Forensics
3.25
article

AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab

Song Liao, Jeannette Wade, Keyan Guo, Hongxin Hu, Ebuka Okpala, Xiaohong Yuan, Sajad Khorsandroo, Nishant Vishwamitra, Long Cheng
article en
1 citations

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 cyberharassment, 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 prior knowledge of AI. Because of this, we were motivated to develop a hands-on lab platform that provided experiential learning experiences to non-computing students with little or no background knowledge in AI. We also discussed the lessons learned in developing this lab for non-computing students. In this lab used by social science students at North Carolina A&T University across two semesters (spring and fall) in 2022, students are given a detailed lab manual and are to complete a set of well-detailed tasks. Through this process, students learn AI concepts and the application of AI for cyberharassment detection. Using pre-and post-surveys, we asked students to rate their knowledge or skills in AI and their understanding of the concepts learned. The results revealed that the students moderately understood the concepts of AI and cyberharassment.

Journal of The Colloquium for Information Systems Security EducationVol. 12(1)
Texas Tech University (US), University of North Carolina at Greensboro (US), North Carolina Agricultural and Technical State University (US), The University of Texas at San Antonio (US), University at Buffalo, State University of New York (US), Clemson University (US)
Openalex Percentile: Top 10%
Digital and Cyber Forensics
3.25
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