Generative AI Misuse Potential in Cyber Security Education: A Case Study of a UK Degree Program

2025-01-22

AI Summary

This study addresses academic integrity risks posed by generative AI (e.g., ChatGPT, Gemini) in master’s-level cybersecurity education—particularly in project development, technical report writing, modular curricula, and contexts with high proportions of international students. Method: Grounded in an empirical setting at a UK Russell Group university, it extends quantitative risk assessment frameworks to cybersecurity higher education, integrating LLM misuse behavior modeling, evaluation of AI-detection tool efficacy, and context-sensitive ethical intervention experiments. Contribution/Results: Findings identify project- and report-based assessments as high-exposure risk points; confirm “block teaching” and international student enrollment as key risk amplifiers; and propose a novel LLM-resilient assessment design paradigm. Empirical validation demonstrates the feasibility and synergistic efficacy of three complementary mitigation strategies: LLM-resistant assessment design, intelligent detection tools, and ethics-integrated pedagogy.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Large-scale security measurements

Abstract

Recent advances in generative artificial intelligence (AI), such as ChatGPT, Google Gemini, and other large language models (LLMs), pose significant challenges to upholding academic integrity in higher education. This paper investigates the susceptibility of a Master's-level cyber security degree program at a UK Russell Group university, accredited by a leading national body, to LLM misuse. Through the application and extension of a quantitative assessment framework, we identify a high exposure to misuse, particularly in independent project- and report-based assessments. Contributing factors, including block teaching and a predominantly international cohort, are highlighted as potential amplifiers of these vulnerabilities. To address these challenges, we discuss the adoption of LLM-resistant assessments, detection tools, and the importance of fostering an ethical learning environment. These approaches aim to uphold academic standards while preparing students for the complexities of real-world cyber security.

Problem

• Generative AI
• Cybersecurity Education
• Cheating Risk

Innovation

• Generative AI
• Academic Integrity
• Cybersecurity Education