Conference Agenda

The rapid proliferation of Generative Artificial Intelligence (GenAI) tools, particularly large language models (LLMs) such as ChatGPT, Google Gemini, and Anthropic Claude, has introduced a transformational inflection point for higher education (HE) research environments globally. Within just two to three years of mainstream availability, these systems have moved from objects of curiosity to embedded features of academic workflows, raising urgent questions about their implications for the quality, integrity, equity, and governance of scholarly knowledge production. This systematic literature review synthesises findings from thirteen peer-reviewed and scholarly sources published between 2023 and 2025, mapping the emerging landscape of GenAI-driven research across four analytical dimensions: (i) the technical capabilities and potential of contemporary GenAI systems; (ii) the research methodology implications for both qualitative and quantitative inquiry; (iii) the substantive opportunities presented to researchers, educators, and institutions; and (iv) the challenges and ethical concerns threatening academic integrity, equity, data privacy, and institutional governance. Drawing on a thematic analysis underpinned by a PRISMA-informed selection framework, this review finds that GenAI offers substantial productivity gains across the entire research lifecycle, from automated literature synthesis and hypothesis generation through data analysis, writing, peer review, and dissemination. Simultaneously, it introduces acute risks relating to AI-generated misinformation (hallucinations), academic dishonesty, algorithmic bias, governance deficits, and unequal access. The paper concludes by proposing a five-stage Institutional Maturity Model and a cross-cutting seven-principal governance framework to guide responsible GenAI integration in higher education research, and by identifying priority directions for future empirical inquiry.

Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support tools in educational settings. However, their reliability for students working in non-English languages, especially low-resource languages, remains poorly understood. We examine this gap by evaluating mathematical reasoning in Sinhala and Tamil---two languages widely used in South Asian schools but underrepresented in artificial intelligence (AI) research. Using a taxonomy of six math problem types, from basic arithmetic to complex unit conflict and optimization problems, we evaluate four prominent large language models. To avoid translation artifacts that confound language ability with translation quality, we construct a parallel dataset where each problem is natively authored by fluent speakers with mathematical training in all three languages. Our analysis demonstrates that while basic arithmetic reasoning transfers robustly across languages, complex reasoning tasks show significant degradation in Tamil and Sinhala. The pattern of failures varies by model and problem type, suggesting that strong performance in English does not guarantee reliable performance across languages. These findings have direct implications for the deployment of AI tools in multilingual classrooms, and highlight the need for language-specific evaluation before adopting large language models as math tutoring aids in non-English educational contexts.

Generative Artificial Intelligence or GenAI is
becoming an important tool in healthcare, offering new ways to
support clinical work, improve patient services, and assist with
administrative tasks. However, its adoption in small and midsized
healthcare enterprises in the Philippines remains limited. These
organisations operate with weak digital systems, limited funding,
low digital skills, and uneven governance structures. This paper
presents a systematic review that examines the factors influencing
GenAI adoption in Philippine healthcare SMEs. The review
followed the PRISMA 2020 guideline and used the Critical
Appraisal Skills Programme checklist to ensure quality. A total of
thirty-nine (39) studies were included and analysed using thematic
synthesis. Four main domains were identified which are barriers,
enablers, ethical and governance issues, and organisational
readiness. These domains provide the foundation for a future
framework that will be developed after fieldwork. This study
offers an early scientific base to support responsible and realistic
GenAI adoption in low resource healthcare settings in the
Philippines.

Small and Medium Enterprises (SMEs) in regional Australia face structural barriers, including geographic isolation, volatile energy costs, thin local markets, and persistent digital‑connectivity gaps, that complicate the uptake of renewable energy technologies. Although microgrids, rooftop photovoltaics, battery storage, and standalone power systems offer increasing technical and economic viability, many SMEs lack the energy literacy and decision‑readiness needed to act. This paper presents a systematic literature review with thematic analysis examining how generative artificial intelligence (GenAI) can support SME energy decision‑making in regional and remote contexts. Drawing on multidisciplinary peer‑reviewed and policy‑relevant sources spanning regional energy systems, SME adoption behaviour, digital inclusion, and GenAI‑enabled education, the review identifies seven themes covering capability barriers, digital constraints, GenAI pedagogical affordances, advisory translation, policy settings, First Nations leadership, and cross‑cutting evidence gaps. Synthesising Australia‑specific microgrid and policy evidence with contemporary GenAI education research, the paper proposes a five‑stage GenAI‑for‑energy‑literacy framework. The framework is intended to guide the design of offline‑first, policy‑literate, and culturally safe digital advisory tools that help time‑constrained SMEs translate complex energy options into informed, business‑ready decisions