Ekman S1, Chen W1, Gibson S1, Catney B1, Papaioannou A1
1University of Technology Sydney, Sydney, Australia
Biography:
Serena Ekman
Dr Serena Ekman is the Executive Officer (Research) at the University of Technology Sydney (UTS). In her role at UTS she has assisted in the development and implementation of numerous pan-university strategic initiatives including the use of AI in research, and professional development for researchers and research support and enablement staff. Serena uses her personal experience as both an academic and professional staff member, combined with the experiences of her peers, to shape organisational initiatives using a person-centred approach.
Weisi Chen
Dr. Weisi Chen is currently the eResearch Technology Specialist at University of Technology Sydney (UTS). He has a PhD on computer science and engineering, and has extensive experience with eResearch tools and platforms, research data management, data analytics, AI, and training for over 10 years. He has developed training courses on REDCap and GenAI for UTS; and has been involved in various AI initiatives and projects to enhance research among the UTS researcher community.
Susan Gibson
Susan Gibson is Head of Data Analytics and AI at the University of Technology Sydney, where she leads enterprise-wide AI strategy and delivery. She is responsible for advancing the university’s use of AI across teaching, research and operations, with a focus on practical adoption, strong governance and measurable impact. Susan has led the scaling of AI across a major university, including large scale deployment of Microsoft 365 Copilot, development of AI-powered assistants at scale and the application of Predictive and GenAI in operational decision making and curriculum assurance while establishing the governance and capability needed for responsible, enterprise-wide adoption. Recognised for her sector leadership, she brings a balance of strategic vision and hands-on delivery to drive meaningful transformation.
Bernie Catney
Bernie Catney supports responsible AI governance and operations from her role within the Data Analytics and Insights Unit at the University of Technology Sydney (UTS). She has helped shape UTS's AI Operations Policy and guidelines, which provide the governance foundation for AI use across the university, and collaborates with colleagues in AI enablement and teams across the university to embed safe, responsible AI practices.
Anastasios Papaioannou
Dr Anastasios Papaioannou is a Senior Manager leading the eResearch Platforms and Services at UTS, where he oversees cloud and high-performance computing (HPC), research data storage, training, and the integration of AI in research. He actively works on the adoption of AI by collaborating with UTS colleagues to develop a comprehensive suite of AI guidelines, tools, and training programs for researchers. With a strong background in research, data science, and computational physics, he works closely with academics and HDR students to help them leverage large-scale infrastructure and digital technologies to accelerate their research.
Abstract:
Artificial Intelligence (AI) is reshaping research practice, but many institutions are still navigating how to move from fragmented experimentation to responsible, institution-wide value. The University of Technology Sydney (UTS) is addressing this challenge through a coordinated AI in Research Framework that connects governance, capability, infrastructure, experimentation, and collaboration, offering a practical institutional model to enable responsible AI adoption in research.
This Framework spans six areas: capability and skills development; responsible AI and governance; safe experimentation and adoption of GenAI tools for research; AI adoption and maturity insights; secure, scalable and sovereign AI infrastructure; and collaboration and collective innovation. The Framework works in harmony with UTS's Teaching and Operational units to ensure staff have a seamless experience across all aspects of AI at UTS, enhancing holistic understanding and compliance.
In practice, this includes an AI in Research Hub; targeted GenAI for Research/HDR training; communities of practice; Use of AI in Research Guidelines; a structured procedure to safely evaluate, pilot and adopt GenAI tools; support and resources for use of AI relating to funding, publishing, Indigenous cultural and intellectual property, HDRs, and eResearch; and exploration of secure, scalable, and sovereign AI infrastructure.
To better understand adoption and inform future investment and support, UTS has developed a university-wide AI survey targeting all UTS researchers and HDR students. This presentation will share insights from the Framework and the survey, and discuss how a coordinated, evidence-informed approach can enable responsible AI adoption in research while supporting capability development, reducing risk, and collaboration across institutions.
