Post-secondary Specific Limitations and Risks
While the limitations and risks outlined earlier in this chapter also apply to the post-secondary context, there are several risks specific to our University environment worth considering, specifically supporting academic integrity and equitable access.
(Re)defining academic integrity and academic dishonesty
McMaster’s Academic Integrity Policy defines academic dishonesty as “to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage” and that “it shall be an offence knowingly to … submit academic work for assessment that was purchased or acquired from another source.”
In an article describing how he integrated generative AI into writing assignments, Paul Fyfe observes,
“[C]omputer- and AI-assisted writing is already deeply embedded into practices that students already use. The question is, where should the lines be drawn, given the array of assistive digital writing technologies that many people now employ unquestioningly, including spellcheck, autocorrect, autocomplete, grammar suggestions, smart compose, and others […] within the spectrum of these practices, what are the ethical thresholds? At what point, in what contexts, or with what technologies do we cross into cheating?”[1]
He continues, “educational institutions continue to define plagiarism in ways that idealize originality.”[2] In this observation, Fyfe highlights a recurring theme in the literature around academic integrity and artificial intelligence, that is: with these technologies the defined boundaries of independent work have become porous, and the contrast between “humanity originality and machine imitation”[3] blurs.
The result of this shift in understanding is a call within the literature to reexamine, and perhaps redefine, what constitutes plagiarism, academic integrity and academic dishonesty, with some authors arguing that “Academic integrity is about being honest about the way you did your work”[4], others urging a defended boundary of primarily individual effort[5], and still others arguing for a new framework entirely – what Sarah Eaton calls ‘post plagiarism’ through a norm of human hybrid writing.
Where most of the reviewed literature holds consensus is that using generative artificial intelligence does not automatically constitute academic misconduct[6], but rather, to quote the European Network for Academic Integrity, “Authorised and declared usage of AI tools is usually acceptable. However, in an educational context, undeclared and/or unauthorised usage of AI tools to produce work for academic credit or progression (e.g. students’ assignments, theses or dissertations) may be considered a form of academic misconduct.”[7]
Detection
Questions around detecting AI generated writing fall into:
- the technological – is it possible to reliably detect AI-generated writing?
- the philosophical – is the role of the educator one of trust or one of surveillance?, and
- the existent