Go beyond binary thinking: our challenge at CRADLE’s Symposium 2024
22 October 2024
Co-convened by Professor Margaret Bearman and Dr Jack Walton, CRADLE’s International Symposium 2024 asked How could generative AI change work-integrated learning? The symposium was held from 7-9 October 2024. In this post CRADLE PhD student Associate Professor Susie Macfarlane reflects on the symposium.
It was a privilege to participate in the CRADLE International Symposium 2024 on generative Artificial Intelligence (genAI) and Work Integrated Learning (WIL). Over three and half days we explored the ways in which genAI may interact with students’ learning experiences in placement settings. Conversations embraced the importance of undertaking research that explores the complexity and ambiguity of genAI in the context of WIL, rather than rushing to superficial conclusions or pat solutions.
Initially, genAI was conceptualised through a kind of dualistic comparison with our sense of who we are as humans and how we work, highlighting contrasts such as meaningful vs meaningless, low vs high cognition, menial vs compassionate, hallucinatory vs informed, and generating outputs rather than reflecting the texture of experience. We were then challenged to go beyond binary thinking, noting that Large Language Models have processed more text than any human could read, and that AI now outperforms expert humans in several cognitive domains. I have been contemplating the importance of co-designing research with students that examines the genAI literacies and dispositions require to learn and work with genAI, particularly as we approach Artificial General Intelligence.
A key focus for the symposium delegates was unpacking the significant challenges students experience in the hybrid space of learning and work. The symposium identified the opportunity to harness an AI-driven chatbot to provide support and feedback and encourage reflective practice with students on placement when a human supervisor is not available. These chatbots could offer immediate, ‘empathetic’ responses to students’ concerns, simulating the presence of a mentor and alleviating feelings of isolation during challenging placement experiences (see our WILma prototype). The delegates also identified the need for guides and resources for educators and students that are not prescriptive, but ask questions to promote discussion and co-design of learning (Goodyear’s Design Patterns).
What struck me the most was the centrality of the human experience in our discussions, and that preconceived binaries dissolved as we explored the role of artificial intelligence in addressing human needs.
About Susie Macfarlane
Associate Professor Susie Macfarlane is the Associate Director of Learning Innovation (Health), part of Deakin Learning Futures (DLF). Susie is a Senior Fellow of the Higher Education Academy. Susie is also studying for a PhD with CRADLE. She is investigating evaluative judgement in higher education.
References
Goodyear, P. (2004). Patterns, pattern languages and educational design. In R. Atkinson, C. McBeath, D. Jonas-Dwyer & R. Phillips (Eds), Beyond the comfort zone: Proceedings of the 21st ASCILITE Conference (pp. 339-347). Perth, 5-8 December. http://www.ascilite.org.au/conferences/perth04/procs/goodyear.html
Trede, F., Markauskaite, L., McEwen, C., & Macfarlane, S. (2020). Education for Practice in a Hybrid Space. Springer Singapore. https://doi.org/10.1007/978-981-13-7410-4
Leffer, L. (2024, June 25). In the Race to Artificial General Intelligence, Where’s the Finish Line? Scientific American. https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/
OpenAI. (2024). ChatGPT (October 2024) [Large language model]. https://chatgpt.com/share/67147e4f-0520-800c-9ecb-224b74e878bf
OpenAI. (2024). Learning to Reason with LLMs. OpenAI. https://openai.com/index/learning-to-reason-with-llms/
