In recent years, the benefits of using AI for education have gained traction, driven by technology advantages for hybrid setups during and following the COVID-19 pandemic.
AI’s role in enhancing learning experiences, streamlining administrative processes, and fostering innovation in higher education is expected to contribute to the forecasted growth of the global e-learning market to US$465 billion by 2028.
However, in the Australian education sector, adopting AI as well as ensuring AI security presents unique challenges. To ensure successful AI integration, addressing core problems is crucial. We discuss key challenges and recommend strategies for higher education centres to wield AI technology securely and effectively.
Challenges in Australian Higher Education
Gartner has predicted that by 2028, more than 70% of teaching, research, and student-submitted material for all educational levels will be supported by generative AI (GenAI).
However, this prediction also acknowledges data-related concerns, as the same report anticipated that only less than 15% of global school systems will have achieved the data governance, management, and readiness that is vital for AI to deliver sustainable transformation in education.
Similarly, Australia’s higher education institutions face significant challenges with data management.
Deloitte reports that tertiary education providers are navigating amplified demands for digital transformation, including personalised communications and tailored learning experiences. Despite demand for better, AI-integrated e-learning systems, data silos, inconsistent data formats, and outdated information can impede the effectiveness of AI initiatives.
Strategic Data Priorities for AI Implementation
To effectively harness AI and establish AI confidence, Australian universities must address these challenges through targeted improvements in five key areas:
1. Optimise Data for Educational Outcomes
High-quality data is the backbone of effective AI applications. Accurate, consistent, and accessible data enables AI systems to function optimally, providing reliable insights and predictions.
In education, enhancing data calibre empowers institutions to better tailor educational content to individual student needs, supporting improved learning outcomes. Accurate data reduces time on cleaning and preparing syllabi, allowing staff to focus on strategic tasks while expediting processes like admissions.
Well-managed data helps identify students who need additional support – such as peer-assisted study sessions, wellbeing support, or financial assistance – by pinpointing learning gaps and using systematic data filtering and analysis by learning which students may need additional support, thereby promoting equitable education. High-quality data facilitates accurate feedback about students' abilities and improvement areas, fostering an environment where individuals can maximise their strengths while progressing.
Reliable data offers a forward-looking perspective by generating insights for informed decision-making, enabling relevant analyses of job market trends for personalised career advice.
2. Develop Robust Data Governance Frameworks
A robust data governance framework ensures data accuracy, consistency, and accessibility — essential foundations for successful AI integration. Having such structure supports compliance with Australian data privacy laws, including updates to Australia's Privacy Act reforms and Australian Privacy Principles (APPs).
Institutions can execute thorough AI security by creating access controls in digital workspaces, such as Microsoft Teams and SharePoint, for example, to ensure that highly sensitive information, such as student information, are safeguarded against misuse while also maintaining transparency and accountability. This creates inclusive educational environments through smart data management. Role-based permissions ensure only authorised staff access relevant student data, while audit logs provide transparency. Students maintain direct access to their records, and the system ensures accessibility across platforms. The outcome? Universities maintain regulatory compliance while enabling effective collaboration across support services.
3. Elevate Technological Infrastructure
Just imagine how many thousands of students enrol in Australian higher education institutions each year. By December 2024, international student enrolments reached an estimated 1.1 million, marking a 15% increase from 2019. Similarly, domestic student enrolment increased in early 2025, with many institutions reporting increases ranging from 4% to more than 20% in new enrolees, especially in education and nursing disciplines.
Organisations must invest in cloud platforms to gain advanced computing capabilities, scalable storage solutions, and reliable networks. These cloud benefits help support AI initiatives, which demand significant computational power to process large datasets and run complex algorithms.
Cloud platforms also reduce upfront infrastructure investments through pay-as-you-go models, optimising costs and resource usage. Cloud platforms help in:
- Facilitating collaboration by providing easy access to shared resources.
- Protecting sensitive data through advanced security.
- Scaling resources based on demand, ensuring efficient handling of varying workloads.
4. Create a Sense of Shared Accountability
Empowering people is key to AI security and AI confidence in higher education. This starts with building confidence, clarity, and collaboration across every level of the institution.
For AI to be successful in Australian higher education, staff and faculty must be empowered to efficiently fulfil their roles. This begins with accessible training programmes that demystify AI concepts and build practical skills without requiring technical expertise.
Universities can:
- Utilise AI tools with intuitive interfaces that integrate with existing systems to build the rapport with AI.
- Establish AI champions within departments to provide peer support for colleagues.
- Create regular feedback channels to help ensure that AI implementations address actual pain points.
- Allow staff to experiment within safe boundaries through governed solutions with appropriate permission structures.
- Establish clear roles and user access to promote transparency and traceability
- Implement regular monitoring and assessment of AI systems by cross-functional teams to identify potential security issues.
Shared accountability among all university stakeholders is vital. Just as public service entities committed to efficiency must draw from the Protective Security Policy Framework (PSPF) principles, Australian universities should establish clear roles and responsibilities for AI governance that promote transparency and traceability. These accountability structures help manage risk while encouraging innovation.
Regular monitoring and assessment of AI systems by cross-functional teams ensures potential security issues are identified and addressed proactively.
By embedding shared accountability practices into university governance frameworks, Australian higher education institutions can scale their data and AI operations responsibly while maintaining necessary security controls. This balanced approach builds trust essential for broader AI adoption.
5. Secure Resources for Sustainable AI
While AI offers immense potential, turning that potential into reality requires more than just vision: It demands the financial backing to build, scale, and sustain meaningful innovation.
Think of trying to scale AI across a university without the right financial support — it’s like building a high-speed train on a dirt road. AI success in higher education hinges on smart, sustained investment. But for many Australian institutions, tight budgets and competing priorities make this a real challenge.
To move forward, universities need to tap diverse funding streams, such as government grants aligned with national digital strategies, industry partnerships bringing both funds and expertise, and alumni contributions supporting innovation. But funding's just part of the puzzle. Universities must also invest in high-impact, low-complexity tech. Low-code/no-code platforms are perfect examples as they create tailored AI solutions without deep programming skills, making digital initiatives more affordable and flexible.
Now picture what’s possible with the right funding: scalable infrastructure, pilot programmes that test real-world use cases, and confident, AI-literate staff. Performance-based funding models can also help — rewarding outcomes like improved student retention or streamlined operations.
And here’s the key: Tracking the impact of these investments builds a strong case for continued support. When institutions can show how AI improves learning, boosts efficiency, and supports student success, it’s easier to justify future funding.
In short, a well-rounded investment strategy – backed by real results – ensures AI adoption isn’t just possible, but powerful, sustainable, and aligned with the future of Australian higher education.
Laying the Foundations for AI-Ready Education
Addressing the core problems of data quality, technological infrastructure, shared accountability, training, and funding is essential for successfully integrating AI confidence in higher education.
More importantly, these enhancements should not be approached as fragmented areas. Instead, universities and tertiary learning centres in Australia should treat them as interdisciplinary cornerstones for sustainable AI implementation across the higher education landscape.
