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This guideline document seeks to support the implementation of MTU policies and procedures which underpin Academic Integrity and help to address the implications of Generative Artificial Intelligence on assessment practices.
The guideline document sets out six options which can be used by students to demonstrate for their lecturers, their commitment to academic integrity in the submission of their assessment work. The options set out below can be used independently or combined as deemed appropriate. Lecturers can determine how best to use the guideline options depending on the nature of the formative or summative assessment under consideration.
Creative Commons Attribution-ShareAlike (CC BY-SA)
This work is licensed under a CC BY-SA license, allowing adaptation and sharing with proper attribution, provided derivative works use the same license.
https://creativecommons.org/licenses/by-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 open educational resource provides a step-by-step guide to cataloguing three-dimensional physical objects — primarily material samples — in Koha, the open-source Library Management System.
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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