Generative AI and the Transformation of Higher Education: Challenges, Opportunities, Best Practices
Generative AI is a rapidly developing field that holds great promise for transforming the way we design, create, and interact with technology in higher education. As with any transformative technology, there are complex considerations that must be taken into account. In this session, we will explore generative AI from three perspectives: engineering, data, and learning experience (LX). What technical considerations come into play when developing a generative AI system (scalability, reliability, architectures)? How can we make incremental progress toward positioning our systems and data in order to leverage generative AI? How do we manage and store large amounts of data in an ethical and secure way that aligns with the values of institutions? How can generative AI be used to enhance the teaching and learning experience for students and faculty? What are the potential challenges? How do we ensure that the generated content is accessible, inclusive, and appropriate for diverse audiences? Foundationally, how can we engage in this transformation in a principled way, being vigilant against bias and centering security, privacy, and building trust? Attendees will gain an understanding of the challenges and opportunities presented by generative AI in higher education through these three lenses: engineering, data, and LX. Participants will leave with practical insights and considerations that they can immediately apply to their conversations and decision making around generative AI.
Presenters
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Kate Giovacchini
Managing Director, Trusted Learner Network,
Arizona State University
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Allison Hall
Senior Director, Learning Experience,
Arizona State University
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Elizabeth Reilley
Senior Direction of Data and Analysis,
Arizona State University