Transforming Legacy Systems in Modern Technological and Government Industries through Advanced Artificial Intelligence and Machine Learning Models

About this project 

To conduct a comprehensive systematic review into modernising legacy systems in technological and government industries, with the objective to research on the implementation of advanced AI and ML models, emphasising the importance of addressing generational dynamics and fostering an inclusive approach to technological transformation. The methodology of the research will be to adopt a mixed-methods approach, combining qualitative and quantitative research methods. Qualitative research will involve case studies of technological and government industries with legacy systems to understand specific challenges and requirements and to achieve a systematic review. Quantitative research will involve developing and testing AI and ML models on selected legacy systems to measure improvements in performance, security, integration, and connectivity. Data will be collected through interviews with IT professionals, managers, and cybersecurity experts in technological and government industries to gather insights into the challenges of legacy systems. System analysis will be conducted on selected legacy systems to identify key areas for AI and ML intervention. Data analysis will include qualitative thematic analysis of interview data to identify common challenges and needs, and quantitative performance, security, integration, and connectivity metrics before and after implementing AI and ML models to evaluate improvements, and the cross-evaluation of AI agents.

Context of People Aspect and Generational Gap:

• Understanding and Inclusion: It is crucial to understand and include all generations in the modernisation process. Ignoring older generations, such as Baby Boomers and Generation X, who have valuable experience and knowledge, can lead to resistance and loss of critical institutional knowledge.
• Training and Adaptation: Developing training programs to help older generations adapt to new technologies is essential. This can bridge the generational gap, ensuring smooth integration and reducing resistance to change.
• Engaging Generation X: Given that Generation X holds many senior management positions, it is vital to engage this group actively. Addressing their concerns, demonstrating the benefits of modernisation, and involving them in the transition process will be key to overcoming resistance.
• Collaboration: Encouraging collaboration between generations can foster innovation and improve problem-solving by combining the experience of older employees with the fresh perspectives of younger ones. There is the aforementioned research environments, and the adjoining areas of improvement in government, industry and private sectors.

What you’ll do 

This outcome will be to produce research both at a systematic review level and application level of AI and ML implementation. It will provide a comprehensive framework for modernising legacy systems in technological and government industries through advanced AI and ML models. By addressing the unique challenges of these sectors and considering the generational dynamics, the study will offer practical solutions to improve operational efficiency, security, integration, connectivity, and workforce cohesion, thereby enabling these companies to remain competitive and innovative in a rapidly evolving technological landscape. Also, to produce insights on People Aspect and the Generational Gap, this means there is a focus on understanding and inclusion; training and adaptation; and collaboration of the work-force. This will have an impact on the various groups ranging from Industry, public sector (government) and private sectors.

Future impact is envisaged to stem from both Government and Industry advancements:

• Operational Efficiency: The AI and ML models developed will automate and optimize processes, reducing manual effort and increasing overall efficiency.
• Cost Reduction and Enhanced Performance: By modernising legacy systems, companies can significantly lower maintenance costs and reduce dependency on costly legacy system specialists.
• Scalability and Flexibility: Modern systems are designed to be more scalable and adaptable to changing business needs.
• Improved Data Integration: Enhanced integration capabilities will facilitate seamless data exchange between different systems, improving decision-making processes.
• Connectivity Enhancement: Upgraded systems will ensure better connectivity across various platforms and devices, fostering a more integrated and responsive technological environment.
• Generational Collaboration: Inclusion of all generations will leverage diverse skills and experiences, promoting a more innovative and cohesive workforce.

Where you’ll be based 

UniSA STEM

Nick Fewster-Young: Co-supervised students in Masters and PhD level, with completions in both, and been an advisor and lead on research grant projects and final semester Capstone / Maths Clinic projects. As the Program Director of Data Science, and sitting on the executive committee of HERGA, member of C3L and AI and Data Science groups contributes greatly to the project.

Wolfgang Mayer: Extensive research experience and has supervised masters and PhD students in the IT and AI. He brings significant expertise to the project and will be a co-lead, and ideally a principal supervisor as well.

Malgorzata Korolkiewicz: Has experience in supervising Phd and Masters students as a co-supervisor and externally, in the area of learning analytics, and recently is a member with the others on an AI Educational Development Grant. She contributes excellence in project management, student experience and experience in systematic review, and help enable the student towards completion. Financial Support  

This project is funded for reasonable research expenses.  A fee offset for the standard term of the program is available to Australian and New Zealand citizens, and permanent residents of Australia, including permanent humanitarian visa holders. Additionally, any Australian Aboriginal and/or Torres Strait Islander applicant who holds an offer of admission without a living allowance will be eligible for the Aboriginal Enterprise Research Scholarship. This scholarship is to the value of $52,352 per annum (2025 rate). Any Aboriginal Enterprise Research Scholarship recipient will also receive a fee waiver. International applicants are not invited to apply at this time.

Eligibility and Selection

This project is open to applications from Australian or New Zealand citizens, and Australian permanent residents or permanent humanitarian visa holders. International applicants are not invited to apply at this time.

Applicants must meet the eligibility criteria for entrance into a PhD. 

All applications that meet the eligibility and selection criteria will be considered for this project. A merit selection process will be used to determine the successful candidate.

The successful applicant is expected to study full-time or part-time, and to be based at our Mawson Lakes Campus in the north of Adelaide. 

Essential Dates

Applicants are expected to start in a timely fashion upon receipt of an offer. Extended deferral periods are not available. Applications close on Monday 3 March 2025.