Conference Agenda

This report examines the main concepts in standards relating to information security management systems and provides guidelines for their integration into the educational process. The authors focus on a review and critical analysis of maturity models for these systems, based on the international standard ISO/IEC 27001, empirical research, systematic literature reviews, and case studies from the academic and private sectors. Four criteria for evaluating the models have been identified: validity, applicability, scope, and measurability, on which the approaches differ significantly. A common integrated framework with five levels of maturity is proposed, combining the evaluation criteria, implementation recommendations, and guidelines for future research.

This paper investigates how ethical awareness influences the adoption of generative artificial intelligence tools among university students. A survey of 35 third‑year Information Systems students at a South African university was conducted after implementing a mandatory AI disclosure and reflection framework within a course assignment. The research model integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with virtue ethics constructs including responsibility, fairness and prudence. Using a Partial Least Squares Structural Equation Modeling (PLS‑SEM) approach, the results indicate that ethical awareness constructs have a stronger influence on behavioral intention to use AI than traditional technology adoption factors. The findings contribute to the growing discussion on Responsible AI education and suggest that structured pedagogical interventions can foster reflective and accountable engagement with generative AI technologies.

Generating MCQs from source code is a scalable approach to assessing programming skills, yet current rule-based and LLM-driven methods often produce weak distractors or semantic errors. Rather than a broad systematic review, this work analyzes sixteen key papers across automated assessment, code understanding, question generation, and LLM-based tools. We identify a recurring lack of integration between program analysis and controlled generation. To address this, we propose GenMCQ, a framework that combines code parsing with multi-stage verification. Our results suggest that this architecture establishes a more reliable basis for LLM-assisted assessment tools.

Artificial Intelligence (AI) is transforming digital business by enabling intelligent automation, predictive analytics, and adaptive decision-making. Despite strong digital adoption, Australian small and medium-sized enterprises (SMEs) remain constrained in transitioning from traditional e-business models to AI-driven business systems. This study develops an AI-business transformation framework tailored to SMEs using a PRISMA-based systematic literature review and a Design Science Research (DSR) approach. The review synthesises global and Australian literature to identify key drivers, barriers, and adoption patterns influencing AI integration. Findings indicate that while SMEs demonstrate high levels of digital readiness, AI adoption is limited by capability gaps, fragmented data infrastructure, financial constraints, and governance challenges. Based on these insights, a multi-layered framework is proposed, integrating data management, AI and analytics capabilities, application systems, and strategic governance. The framework provides a structured and scalable pathway for SMEs to achieve AI-driven transformation. This research contributes to both theory and practice by offering a validated foundation for AI-business development in SME contexts.