Conference Agenda

GenAI integration in the classroom has largely been defensive, centered on detection and academic integrity. However, these individual-focused assessment models fail to mirror the collaborative nature of modern software development. In this paper, we advocate for a new framework: the Human-AI Team (HAIT). By treating GenAI as a formal team member in structured classroom settings, we can shift the focus from the finished artifact to the journey of building it. This approach turns students into critical reviewers, reducing ``algorithmic loafing'' and ensuring code is not submitted unthinkingly. Ultimately, this framework allows educators to verify true technical comprehension without stripping students of the tools they will use throughout their careers.

Generative AI (gen-AI) tools are reshaping academic integrity and assessment design in Australian higher education. Following the Tertiary Education Quality and Standards Agency’s (TEQSA) 2024 directive that every provider files a gen-AI action plan, universities and non-university higher education providers (NUHEPs) released a wave of new policies. This paper develops a data-driven foundation and future oriented roadmap for generative AI policy in Australian higher education. We catalogue 52 institutional policies and examine key policy patterns related to governance, academic integrity, and assessment practices. The study highlights emerging patterns in institutional responses, including variations in governance approaches, evidence standards, and support mechanisms. A ten-point future-oriented policy roadmap and a future research agenda are proposed, aligning with the broader theme of “Intelligence and Sustainability in Connected Systems” by highlighting how systemic governance and collaborative approaches can enable sustainable and scalable integration generative AI in higher education.

Generative Artificial Intelligence (GenAI) is gaining
attention in healthcare for its potential to support documentation,
summarisation, communication, and selected administrative
tasks. However, adoption in Philippines healthcare small and
midsized enterprises remains limited due to persistent operational
and structural constraints. This paper presents a synthesis of
evidence on the organisational readiness of healthcare SMEs in
the Philippines to adopt GenAI responsibly. Drawing on thirty
nine peer reviewed studies identified through a systematic review,
the paper integrates two complementary perspectives: the barriers
and enablers influencing adoption, and the readiness conditions
required before implementation. The analysis shows that
readiness is shaped by four interrelated dimensions: digital,
workforce, financial, and governance readiness. Across these
dimensions, major barriers include weak infrastructure,
fragmented workflows, limited digital capability, constrained
budgets, and underdeveloped governance practices. At the same
time, leadership support, learning capacity, institutional
partnerships, and alignment with national digital health initiatives
emerge as important enabling conditions. Based on these findings,
the paper proposes a contextualised readiness perspective in
which barriers and enablers interact to influence the capacity of
healthcare SMEs to adopt GenAI in a safe, sustainable, and locally
appropriate manner. The study contributes a more integrated
understanding of adoption readiness in low resource healthcare
settings and offers a foundation for future empirical validation and
implementation planning in the Philippines context

Artificial Intelligence (AI) rapidly transforms digital commerce by enabling intelligent automation, predictive analytics, and adaptive decision-making within small and medium-sized enterprises (SMEs). Despite increasing interest in AI adoption, many SMEs continue to rely on traditional e-business models and lack structured approaches to integrating AI-driven systems. This study investigates the transition from e-business to AI-business within Australian SMEs through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review synthesises peer-reviewed studies on AI adoption, digital transformation, and SME innovation across global and Australian contexts to identify key drivers, barriers, and implementation patterns. The findings indicate that while Australian SMEs demonstrate strong digital readiness, AI adoption remains constrained by skills shortages, financial limitations, fragmented data infrastructure, and governance challenges. Based on the synthesis, this study proposes a multi-layered AI-business transformation framework integrating data architecture, machine learning capabilities, and intelligent decision-support systems. The framework provides a structured pathway for SMEs to transition toward AI-enabled digital commerce. This research contributes to both theory and practice by offering a scalable model and actionable insights to support sustainable AI adoption in SMEs. This study contributes by integrating PRISMA-based systematic review with Design Science Research principles to develop a scalable AI-business transformation framework tailored to SME constraints,