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This is a collection of supporting articles and videos that help staff to understand the relationship between Generative AI and Academic Integrity. These resources help staff to make sense of how authorship on assignments might be identified and understood.
Staff can use these resources to gain a better understanding of Generative AI from the perspective of Academic Integrity, and to identify how metadata on a document can tell a story about a works production history.
Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)
This work is licensed under a CC BY-NC-SA license, allowing sharing and adaptation for non-commercial use with proper attribution, provided derivative works use the same license.
https://creativecommons.org/licenses/by-nc-sa/4.0/If you have modified or adopted this resource, share your version here. Tracking adaptations helps us measure impact and connects others with useful updates.
This OER is targeted at lecturers and describes an inclusive framework to integrate artificial intelligence (AI) learning design at third level.
This report draws on Patricia Gibson’s research around critical approaches to Artificial Intelligence (AI) in Education and the increasing automation of Educational Technologies (EdTech).
This resource was created to share the learning from teaching adults with intellectual disabilities (ID) in IADT college as part of a national suite of pilot projects.
Comparing student and educator perceptions of generative artificial intelligence for teaching, learning and assessment: results from two institutional surveys
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