Integrating Generative AI in Cybersecurity Education: Case Study Insights on Pedagogical Strategies, Critical Thinking, and Responsible AI Use
Project Overview
The document examines the role of Generative AI (GenAI) in enhancing cybersecurity education, emphasizing its capacity to bolster critical thinking, problem-solving abilities, and regulatory awareness among students. It presents a structured framework for incorporating GenAI tools into tutorials and assessments, highlighting the necessity of maintaining a balance between AI utilization and human oversight. The findings reveal that GenAI can significantly streamline content creation and elevate student engagement; however, it also notes challenges such as the risk of over-reliance on AI and the disparity in students' familiarity with AI technologies. The study advocates for an integrated approach that leverages AI insights while prioritizing critical human judgment to maximize educational outcomes, ensuring that students develop essential skills without becoming dependent on automated solutions.
Key Applications
Generative AI tools (e.g., ChatGPT, Gemini, Claude)
Context: Cybersecurity education for undergraduate and postgraduate students
Implementation: GenAI was integrated into tutorial exercises for iterative learning and assessment tasks for real-world application, requiring students to generate, critique, and refine AI outputs.
Outcomes: Enhanced critical thinking and analytical skills, improved understanding of regulatory compliance, and increased student engagement.
Challenges: Over-reliance on AI-generated content, variability in AI literacy among students, and contextual limitations of AI outputs.
Implementation Barriers
Technological Barrier
AI-generated content often lacks contextual specificity and regulatory depth, requiring manual refinement by students. Some students relied too heavily on AI-generated outputs without adequate critique or refinement.
Proposed Solutions: Implement structured guidance on AI usage and critical evaluation of AI outputs. Incorporate structured reflection exercises to help students identify gaps in AI outputs and emphasise the necessity of human oversight.
Educational Barrier
Variability in students' familiarity with AI tools led to uneven engagement and proficiency levels.
Proposed Solutions: Provide targeted scaffolding and guided tutorials for students less experienced with AI.
Practical Barrier
Logistical challenges in coordinating real-world business engagements limited student interaction with industry stakeholders.
Proposed Solutions: Create structured business engagement templates and formalise industry collaborations.
Project Team
Mahmoud Elkhodr
Researcher
Ergun Gide
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mahmoud Elkhodr, Ergun Gide
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18