Prof Irwin King
Department of Computer Science and Engineering
The Chinese University of Hong Kong
The Evolution and Future
of Generative AI in Education
1000 - 1040 (UTC+8) @ CUHK
Generative AI is transforming the world at an unprecedented pace, and its impact on education is no exception. As the capabilities of AI continue to grow, the potential for disruption in all areas of the labor market is significant. According to a Goldman Sachs report, up to 18% of global jobs could be entirely replaced by AI, while many more could be complemented by AI-powered tools. What does this mean for our current and future students? The next chapter of education lies in empowering young learners to embrace and develop their uniquely human qualities – those unlikely to ever be replaced by technology. In this presentation, we will explore the evolution and future of generative AI in education, its capabilities and limitations, and the potential risks and opportunities it presents for learners, educators, and society as a whole.
and the Future of Education
Symbi(ai)tic thinking: New onto-epistemological understandings of generative AI for teacher education and beyond
1040 - 1120 (UTC+8) @ Zoom Online
The last decade has witnessed unprecedented and ubiquitous growth in digital technologies, with a particular explosion of artificial intelligence (AI) based technologies such as generative AI across all of society, with significant implication for education. Generative AI is now shaping how we know, how we play, how we learn and how we do our work. While generative AI is already shaping education and the work of teachers, and frameworks around education and the incorporation of AI literacy into teacher education are being proposed, this is an area of research and practice that requires deeper theorising and clearly more research. I argue that with the explosive development of generative AI, the current ways of knowing and learning about technology are outdated and overly static. Frameworks and models (including TPACK) which shape our understandings and the relationship between technologies and humans are largely hierarchical but may not be useful in times of profound change. In this keynote I position generative AI and its affordances, challenges, and dangers in teacher education and the preparation of new educators who will face emerging technological change and challenges in schools and classrooms into the future. Using critical dialogic inquiry and informed by post-humanism, critical literacy, and the metaphor of symbiosis, I propose a set of onto-epistemological understandings built around the notion of the way generative AI relates to humans and their intentional activities, especially in education. The construct of the symbi(ai)tic is introduced to encompass these understanding. This construct allows for a more adaptive and fluid approach in an AI-infused world and includes ethical understandings that are essential in conceiving generative AI in teacher education and more broadly across society.
Senior Lecturer / Associate Professor
Dr Edwin Creely
School of Curriculum, Teaching and Inclusive Education
Prof David Carless
Faculty of Education
The University of Hong Kong
Assessment and Feedback Redesigns for
the Generative AI Era
1425 - 1505 (UTC+8) @ CUHK
Generative AI represents opportunities and threats for assessment in higher education. Opportunities lie in building on key principles underpinning good ‘assessment’: iterative sequences of rich tasks; the development of student evaluative expertise; linkages to real-world outcomes; and feedback designs for principled student follow-up action. Effective assessment sequences, however, can be time-consuming so an essential starting point for reform is to reduce assessment overload. When students are juggling multiple assessment deadlines, quality learning is undermined and malpractice becomes tempting.
By reducing assessment overload, teachers in higher education create much-needed space for new possibilities: increased authentic assessment; assessments that involve critical engagement with generative AI outputs; an enhanced role for digital oral assessments; and assessing process as well as product.
Generative AI also extends feedback seeking, implying a need for shared development of student and teacher automated feedback literacies. Students need to appreciate what generative AI is effective and less effective at doing; frame appropriate prompts; continue dialogue as necessary; and execute principled follow-up actions to enhance their work-in-progress. Whilst some students may have the skills and dispositions to use generative AI appropriately, others may need more support so teacher guidance and modelling plays a significant role, suggesting a role for teacher and student co-learning in partnerships. The thorny issues of academic integrity and ethical use of generative AI also merit attention but should not distract from a primary focus on student learning.
Generative AI raises exciting possibilities but as yet there are few answers, so I conclude with some recommendations and potential future research directions.