Embracing Generative AI in Higher Education: Navigating the Transition in AUT

Date
2024-09-04
Authors
Harris, Geri
Morrow, Jeremy
Williams, Michelle
Griffiths, Chris
Parameswaran, Prabhash
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
University of Otago
Abstract

The emergence of generative artificial intelligence (GenAI) has prompted significant changes in higher education. We are undergoing a profound transformation, prompting universities to reevaluate pedagogical and assessment strategies. In this short paper, we adapt the Kuebler-Ross Five Stages of Grief to reveal how Auckland University of Technology (AUT) recognise GenAI as an indispensable part of modern education. The paper uses empirical accounts from early AI adopters to show that AUT is at a place of acceptance: embracing AI not just as a tool but as a transformative force. AUT educators are adapting to this new technology, despite traditional resistance to change of teaching practices. Accounts of integrating GenAI into learning, teaching and assessment demonstrate that our educators are taking the lead in guiding this transformation. We are not blind to gen AI's flaws which include bias, transparency, and privacy. But we debate, explore and upskill ourselves to address these concerns. Our students keep pushing us forward in this cycle through anger at GenAI’s arrival to acceptance that it is here to stay, and we must continue to find ways to move forward in collaboration with our learners.

Description
Keywords
AI in Education , technology acceptance , ; improved learning outcomes , assessment reform
Source
Harris, G., Morrow, J., Williams, M., Griffiths, C., & Parameswaran, P. (2024, September 4). Embracing Generative AI in Higher Education: Navigating the Transition in AUT. Paper presented at the New Zealand AI in Higher Education Symposium - Connecting New Zealand’s universities to pioneer the future of AI-enhanced education, Castle 2 Lecture Theatre, University of Otago. https://events.otago.ac.nz/aihe-2024/
DOI
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NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version)