NOMINATION HIGHLIGHTS

Nicki James Shepherd is a Lecturer in Computing at Burnley College, Lancashire, where he teaches college-based higher education students. Between December 2024 and May 2026 he published seven peer-reviewed papers, two of which directly address cybersecurity education and risk.

His 2024 paper, “The role of generative AI in social engineering and phishing: implications for security education,” examined how large language models have changed the threat landscape for phishing attacks. The paper argued that generative AI enables attackers to produce convincing, personalised social engineering content at scale, and that existing cybersecurity education frameworks have not kept pace with this shift. The paper proposed updated approaches to security awareness training that account for AI-generated threats.

His 2026 co-authored paper, “Cyber security in hospitals: legal responsibilities,” analysed the legal obligations institutions face when managing cybersecurity risk in sectors where vulnerable populations depend on institutional services. The paper mapped the regulatory landscape across multiple jurisdictions, identifying gaps between what the law requires and what organisations actually do.

Beyond these publications, Nicki James Shepherd’s broader research programme has direct relevance to cybersecurity education. His study of LLM hallucination documented a 32.6% false output rate and identified patterns of recursive misinformation across AI interactions, a finding with clear implications for how students and professionals assess the reliability of AI-generated security intelligence. His analysis of AI explainability requirements across the EU AI Act, GDPR, and UK regulatory regimes produced a risk-based explainability matrix that applies to any sector deploying automated decision-making, including cybersecurity operations.

He teaches these topics to his computing students at Burnley College, integrating current research into classroom practice. His action research study with Level 4 students tested AI-mediated learning approaches and found that different learner profiles benefited from different modes of AI interaction, informing how cybersecurity concepts can be taught more effectively using adaptive methods.

He is also a disabled lecturer who has researched how disabled students use AI tools in higher education, surveying 125 students and finding that the majority use AI strategically to manage their conditions, not to bypass learning. This work has direct implications for how cybersecurity education programmes design their AI policies to be both secure and inclusive.