Distinguished Professor of Learning Analytics; Director, Centre for Learning Analytics, Faculty of Information Technology, Monash University, Australia
Dragan Gašević is Distinguished Professor of Learning Analytics and Director of Research in the Department of Human Centred Computing of the Faculty of Information Technology and the Director of the Centre for Learning Analytics at Monash University. Dragan’s research interests centre around data analytic, AI, and design methods that can advance understanding of self-regulated and collaborative learning. He is a founder and served as the President (2015-2017) of the Society for Learning Analytics Research. He is a recipient of the Life-time Member Award (2022) as the highest distinction of the Society for Learning Analytics Research (SoLAR) and a Distinguished Member (2022) of the Association for Computing Machinery (ACM). In 2019 to 2022, he was recognised as the national field leader in educational technology in The Australian’s Research Magazine that is published annually.
The prominence of generative artificial intelligence (AI) has ignited fervent discussions about its profound implications for education. The integrity of existing assessment approaches in education and the need to invent new ways for assessment have particularly sparked much debate. These concerns have been counterbalanced by enthusiastic appeals to leverage the capabilities of generative AI to enrich learning and teaching practice. Yet, there are many open questions about the implications of generative AI on learning and teaching.
This talk aims to chart this new territory of generative AI in learning and teaching. The talk will first discuss how generative AI can help us to innovate learning and teaching. We will particularly explore ways to innovate assessment and enhance learning and teaching practice. The talk will then look into critical concerns that we need to consider while using generative AI in learning and teaching. We will specifically explore issues of algorithmic fairness and ethics, workload implications, and limitations of generative AI technologies. The talk will conclude by an overview of open challenges that warrant future research and innovation in learning and teaching.