On an intensely luminous April day in Melbourne, the State Library of Victoria became a hub for discussions about artificial intelligence (AI) and its place within higher education. The HEDx forum, hosted by La Trobe University, brought together leaders from universities, industry, and policy to explore how universities can navigate the opportunities and challenges presented by AI. The day was packed with lively debates, insightful panels, and a shared sense of urgency about the future of AI in an education-driven world (or at least, this is my front-footed take). And apologies if some of my understandings are a little slippery, it’s not because I used Notebook LM in the formation of this review (which I did), it’s because the live stream of the event from the StartSpace room at the State Library was patchy (and if you find a mistake, please tell me and I will fix it).
Setting the Stage: A Revolution in Progress
The day began with Professor Theo Farrell, Vice-Chancellor of La Trobe University, drawing parallels between the Industrial Revolution and today’s AI revolution. Although this historical comparison is somewhat clichéd, his remarks did set the tone for the forum: universities must adapt and lead the change. Farrell’s analogy reminded attendees that just as factories adapted to mechanisation, universities must lead the way in AI’s transformative potential.
Session 2: The Global Perspective on AI-First Universities
Paul LeBlanc from Human Systems and President of Southern New Hampshire University joined via live cross to discuss what an “AI-first university” might look like. However, his framing—portraying AI as a competitor to humans—felt overly deterministic and Hollywood-esque. LeBlanc painted scenarios of “AI versus professions” and even “AI versus learning designers,” which was straight out of 2001: A Space Odyssey. This narrative played down the collaborative potential between humans and machines in education. It was clear that a more nuanced conversation was needed—one that focused on partnership rather than competition.
Session 3: Leadership Panel – Charting an Australian Strategy
The leadership panel, moderated by John Dewar, was a highlight of the day. Leaders such as Professor Pascale Quester (VC Swinburne University), Professor Jessica Vanderlelie (DVC Deakin University), and Dan Cockerell (CEO Torrens University) shared their visions for integrating AI into university strategies.
Cockerell emphasised that students expect educational content to be updated quickly and delivered flexibly—areas where AI can play a pivotal role. Vanderlelie stresses the importance of ensuring academic integrity through checkpoints in degree programs, while acknowledging that universities have significant work to do in building staff literacy around AI. Quester warned against “AI washing”—using AI to merely enhance existing processes—and encouraged institutions to explore entirely new approaches to teaching and learning.
The session underscored a key theme: universities must become disruptors within their own domains, using AI not just as a tool but as a catalyst for innovation.
Sessions 4 & 5: Learning from Industry
Patrick Kidd (Future Skills Organisation) led discussions on how tertiary education can learn from industry’s approach to reskilling for AI. While these sessions touched on productivity and leadership, they lacked actionable insights. Yasminka Nemet (Microsoft) offered optimism about the adaptability of higher education but provided little clarity on the specific AI skills industries need.
Despite these shortcomings, one takeaway was clear: universities must act quickly, even if they don’t yet have all the answers. Experimentation is crucial to staying ahead in this rapidly evolving landscape.
Session 6 & 7: Student Support in an AI Age
Dr Tim Renick (Georgia State University) opened discussions on best practices for student support. However, the panel later veered off-topic with conversations about dropout rates and food insecurity—important issues but perhaps outside the forum’s focus on AI.
More relevant insights emerged in Session 7, where Charlsey Pearce (MortarCAPS Data Standard) highlighted how methods such as predictive analytics may enhance student retention rates. Pearce emphasised that without a robust data infrastructure, AI solutions would struggle to succeed. This session highlighted the importance of utilising data strategically to eliminate administrative barriers and enhance student outcomes. I thought this was one of the underexplored areas of the forum. Both infrastructure and reliable, standardised data are key to institutional success (especially on the administrative side of things; the academic side is another story).
Session 8: Riding the Tiger of AI Feedback
Professors Margaret Bearman (Deakin University) and Michael Henderson (Monash University) shared parts of their findings from their study on Student Perspectives on AI, comparing teacher feedback with AI-generated feedback among 7,000 students. Their research revealed that students value both forms of feedback for different purposes. Notably, students did not devalue teacher feedback, despite having access to AI tools.
This session stood out for its practical focus and emphasis on co-designing solutions with students—a refreshing reminder that learners should be at the centre of any technological transformation.
Session 9: Lessons from Arizona State University
Lev Gonick (Arizona State University) shared how ASU has become a leader in using AI to drive institutional innovation. He advocated for creating “accelerator teams” within universities to rapidly experiment with new technologies. Shainal Kavar (La Trobe University) echoed this sentiment but cautioned that partnerships with industry must align with institutional goals.
Session 10: Priorities for Australian Universities
The closing panel brought together experts, including Professor Danny Liu from the University of Sydney and Dr. Susan Zhang from La Trobe University, to discuss how Australian universities can responsibly embrace AI. Liu raised critical questions about trust and transparency in assessments involving AI tools, while Zhang emphasised the importance of making technology seamless for both staff and students.
Professor Jason Lodge (University of Queensland) challenged attendees to rethink their relationship with AI—not as competitors but as collaborators. He argued that learning how to learn will remain a fundamental skill in an AI-driven world.
Finally, Professor Phil Laufenberg from Macquarie University spoke about fostering a culture of experimentation within institutions. He highlighted the ethical implications of autonomous decision-making systems, stressing that human oversight must always remain central.
Key Takeaways from the Forum
- Universities must lead disruption: Institutions should position themselves as disruptors rather than waiting for external forces to dictate change.
- Collaboration over competition: The narrative should shift from “AI versus humans” to “AI with humans,” focusing on partnership and co-intelligence.
- Data is foundational: Without a robust data infrastructure, even the most advanced AI tools will fail to deliver meaningful results.
- Experimentation is key: Universities need safe spaces for rapid experimentation with new technologies.
- Ethical oversight is non-negotiable: Autonomous decision-making systems must always include human oversight to ensure ethical outcomes.
- Student-centric approaches matter: Co-designing solutions with students ensures that technology effectively serves their needs.
A Call to Action
The HEDx forum reminded everyone that while AI offers immense potential, its successful integration into higher education will require strategic leadership, collaboration across sectors, and an unwavering commitment to ethical innovation.
In this era of rapid technological change, one thing is sure: universities play a pivotal role in shaping not only the future workforce but also the future of learning itself. Universities should be the disruptors!
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