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David Herbert

Publications and source records attributed to David Herbert.

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Advancing Inclusivity in Cybersecurity Education: Integrating Intersectionality to Enhance Student Engagement in Australian Higher Education Curriculums Strategies, Barriers, and Future Directions

Australian women, gender-diverse individuals, and culturally and linguistically diverse (CALD) communities are often more susceptible to phishing and other forms of cybercrimes due to factors such as language barriers, limited access to cybersecurity education, and social isolation. These communities encounter substantial obstacles both entering and progressing in the cybersecurity field. In Australia, the Higher Education sector still leans heavily on a largely uniform cybersecurity curriculum, focusing heavily on technical proficiency, overlooking the vital impact of intersectionality and user-centered thinking for boosting student engagement and learning. Without gender inclusivity and proper consideration of intersectionality forms such as CALD, the workforce is deprived of the varied perspectives necessary to tackle today's intricate cybersecurity issues. In this study, we conducted semi-structured interviews with 15 experienced academics teaching and coordinating cyber security programs from a diverse range of Australian universities, covering all states, to explore their perspectives on: i) current strategies for addressing the women, gender-diverse and CALD perspective in cyber security education in the Australian HE sector; ii) barriers to incorporate women, gender-diverse and CALD perspective in cybersecurity curriculums in higher education; iii) future work and support that is needed. Our research highlights a lack of systematic methods for integrating intersectional perspectives into cybersecurity curriculums. In particular, we identified four key barriers and four areas where support and future efforts are needed to address this issue. Our findings offer vital insights that can substantially guide curriculum development in cybersecurity education.

cs.CY

A Multi-Level Agent-Based Architecture for Climate Governance Integrating Cognitive and Institutional Dynamics

Climate governance processes involve complex interactions between heterogeneous citizens, advocacy groups, media actors, and political decision-makers. While agent-based models (ABMs) have been widely used to study environmental policy and socio-ecological systems, many existing approaches focus either on institutional dynamics or individual behavioural mechanisms in isolation. This paper presents a modular multi-level agent-based architecture that integrates empirically grounded cognitive decision models with strategic institutional behaviour within a unified simulation framework. The architecture combines (i) motive-based individual decision-making operationalised through the HUMAT and MOA frameworks, (ii) socially embedded influence processes via demographic homophily networks, and (iii) institutional strategy modules for environmental non-governmental organisations (NGOs), media agents, and politicians. Political decisions emerge from the aggregation of multiple signals, including expert input, public mobilisation, party alignment, and media framing. The model is designed to be empirically calibrated through synthetic populations derived from survey data and and institutional parameters informed through Living Lab stakeholder engagement, and to support scenario-based exploration of climate-relevant land-use governance processes. Rather than presenting empirical results, this paper focuses on the architectural design principles, modular structure, and integration logic of the model. We discuss how this multi-layered approach contributes to the modelling of democratic climate governance and outline pathways for generalization and future validation.

cs.CY

Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models

Recent advancements in artificial intelligence (AI) and machine learning have reignited interest in their impact on Computer-based Learning (CBL). AI-driven tools like ChatGPT and Intelligent Tutoring Systems (ITS) have enhanced learning experiences through personalisation and flexibility. ITSs can adapt to individual learning needs and provide customised feedback based on a student's performance, cognitive state, and learning path. Despite these advances, challenges remain in accommodating diverse learning styles and delivering real-time, context-aware feedback. Our research aims to address these gaps by integrating skill-aligned feedback via Retrieval Augmented Generation (RAG) into prompt engineering for Large Language Models (LLMs) and developing an application to enhance learning through personalised tutoring in a computer science programming context. The pilot study evaluated a proposed system using three quantitative metrics: readability score, response time, and feedback depth, across three programming tasks of varying complexity. The system successfully sorted simulated students into three skill-level categories and provided context-aware feedback. This targeted approach demonstrated better effectiveness and adaptability compared to general methods.

cs.CY