Responding to acute and chronic challenges of AI in Higher Education

By Tim Fawns, Danny Liu (University of Sydney) and Jason Lodge (University of Queensland)
Posted Tue 14 May, 2024

This week’s post features a short recorded conversation between Associate Professor Tim Fawns (Monash Education Academy), Professor Danny Liu (Educational Innovation team, University of Sydney) and Associate Professor Jason Lodge (School of Education, The University of Queensland).

We spoke about some of the issues that institutions need to think about in responding to the TEQSA Request for Information (RFI) on Addressing the Risk of Artificial Intelligence, and the opportunities and challenges of AI more generally.

The TEQSA RFI requires all Australian Higher Education providers to submit a credible institutional action plan, with appropriate oversight and governance mechanisms, that will “provide assurance that students have attained the skills and knowledge reflected by their qualifications.” Our conversation situated thinking around the RFI in the context of broader questions about the kinds of learning we need to account for (including learning how to learn), the limitations of binary thinking (AI as good / bad; assessments as secure / insecure), and how we can find practical ways forward while trying to move towards complexity.

If you enjoyed this conversation, consider exploring other 10 minute chats on Generative AI hosted by Tim – it’s series of short conversations with guests with different kinds of expertise in generative AI and education.

Share your thoughts in the comments

We would be very interested in your thoughts on the ideas that came up in our chat. Your comments will inform Part 2 of our conversation (coming soon…) which we hope will focus even more on practical implications and ways ahead.

Acknowledgements

This conversation is a small part of wider efforts to help the Higher Education sector develop its response to AI. These wider efforts include the development of principles for Assessment reform for the age of artificial intelligence (in collaboration with TEQSA) and three national events, hosted at University of Sydney:

As such, we want to acknowledge the following colleagues who were also part of those efforts and whose insights have informed the thinking demonstrated in this conversation:

Michael Cowling, Adam Bridgeman (round tables and symposium), Trish McCluskey (round tables), Russell Butson (symposium), Sarah Howard, Margaret Bearman, Phillip Dawson, Shirley Agostinho, Simon Buckingham Shum, Chris Deneen, Cath Ellis, Helen Gniel, Rowena Harper, Michael Henderson, Lina Markauskaite, Jan McLean, Carlo Perrotta, Lambert Schuwirth, Christine Slade (TEQSA assessment reform principles)

Associate Professor Tim Fawns

Tim Fawns is Associate Professor (Education Focused) at the Monash Education Academy. His role involves contributing to the development of initiatives and resources that help educators across Monash to improve their knowledge and practice, and to be recognised for that improvement and effort. Tim’s research interests are at the intersection between digital, professional and higher education, with a particular focus on the relationship between technology and educational practice.

Professor Danny Liu (University of Sydney)

Danny is a molecular biologist by training, programmer by night, researcher and academic developer by day, and educator at heart. He works at the confluence of educational technology, student engagement, artificial intelligence, learning analytics, pedagogical research, organisational leadership, and professional development. He is currently a Professor in the Educational Innovation team in the DVC (Education) Portfolio at the University of Sydney.

Associate Professor Jason Lodge (University of Queensland)

Jason’s research concentrates on the application of the learning sciences to education. Specifically, he is interested in the cognitive and emotional factors that influence learning and behaviour and how research findings from the learning sciences can be better used to enhance design for learning, teaching practice and education policy. Jason is also interested in the ways technologies such as artificial intelligence is influencing learning, particularly in terms of the impact of technology on the development of professional ways of being, metacognition, critical thinking and expertise.

Posted in:

Artificial Intelligence, Pedagogical Approaches

Tags:

Artificial intelligence, higher education, TEQSA

Comments

4 responses to “Responding to acute and chronic challenges of AI in Higher Education”

Eliani Boton

15 May 2024

Thank you, Tim, Danny, and Jason, for your insightful contributions! Your discussion highlights the necessity of a radical shift in educational paradigms, moving beyond traditional models towards a more dynamic and adaptive approach. Indeed, we’ve become so entrenched in teaching and assessing learning outcomes that deviating from this framework while ensuring students’ mastery of required skills seems daunting.

Tim’s point about embracing multiplicity in learning methods offers a promising avenue for addressing this challenge, focusing on the ‘how’ of implementation. While educators may be eager to embrace change (don’t we all?), institutional acceptance of alternative teaching and assessment methods may lag behind. However, sessions like this provide invaluable opportunities to explore and innovate within the evolving landscape of AI-enhanced education.

I think that all of this is challenging, but also exciting. It is demanding lots of adaptability and innovation, if we collectively rise to the challenge.

Eliani

Reply
1. Tim Fawns
  
  15 May 2024
  
  Thanks Eliani. I don’t think we need to get rid of entrenched things like learning outcomes but, equally, I think we need to move away from putting all of our eggs in the one basket – whether that’s learning outcomes, grades, accreditation requirements or whatever. Some or all of those things can be useful as part of a bigger puzzle but they should not become the puzzle, in my opinion. That’s also a reason why there are no quick fixes to most educational problems, and why there are always trade-offs, compromises and uncertainties?
  
  Reply
Geoff

20 May 2024

When I studied Physics IB at USyd in 1973 for the first lecture a Carslaw Building theatre attendant brought in a b&w TV, a large U-matic video machine, pressed play, and left. Over the hour the first-year engineering students carpeted the front of the lecture theatre with paper aeroplanes. The next time we had a real lecturer.

Will future educators choose textbooks that incorporate a blend of world-renowned expertise and award-winning teaching, seamlessly merged using tools like Cogniti? Perhaps the Executive Head of School could make these selections across all subjects within the School, freeing them from personnel management tasks except for researchers. Even then, AI research assistants could significantly augment the productivity of researchers, potentially allowing the Executive Dean to oversee the entire Faculty. In fact, with the right tools, the Vice-Chancellor alone might be able to manage the entire institution. No need for middle management.
(Thanks for the rewrite, Copilot)

Reply
Abigail

9 June 2024

Artificial Intelligence (AI) is revolutionizing the education sector by enhancing the learning experience and streamlining administrative tasks. One of the most significant applications is personalized learning, where AI algorithms analyze students’ strengths and weaknesses to tailor educational content. This approach ensures that each student receives the right level of challenge and support, promoting better engagement and understanding.

Moreover, AI-powered tutoring systems provide students with immediate feedback and assistance outside the classroom. These intelligent systems can adapt to individual learning paces and styles, making education more accessible and effective. For instance, language learning apps utilize AI to offer real-time corrections and customized exercises, helping learners improve their skills more efficiently.

In addition to personalized learning, AI is transforming administrative operations. Automated grading systems save educators considerable time by quickly and accurately assessing student work. This allows teachers to focus more on developing lesson plans and interacting with students. Furthermore, AI-driven analytics help schools track student performance and identify those who may need additional support, enabling timely interventions.

AI also enhances accessibility in education. Speech-to-text and text-to-speech technologies assist students with disabilities, ensuring they have equal opportunities to learn. These tools can transcribe lectures in real-time, providing valuable resources for students who are hard of hearing or have learning difficulties.

Overall, AI’s integration into education offers numerous benefits, from customized learning experiences to efficient administrative processes, paving the way for a more inclusive and effective educational system.

Reply