• this only counts as inappropriate behaviour for students, not academic faculty – the stakes are different, so the standards are different, they claim. You know, because students might use GenAI to fraudulently gain credit towards their degrees, while academics could only use it to pollute the entire corpus of digitally-available scientific knowledge including its backdated citation records.
• the digital corpus of academic publishing is now saturated with AI-generated slop. Journal editors are throwing up their hands in despair, overwhelmed with unmanageable quantities of mediocre and possibly fictional research write-ups, AI-generated “frankencitations”, not to mention a shocking homogenisation of topic foci towards AI-centric inquiry. There are enormous real problems and instead we are overwhelmingly funding hobbyist research into speculative applications of a technology suite that has demonstrated very few (I’m not saying zero) meaningful use cases.
• I was also culturally aware enough to understand that the grades my teachers assigned were not facts but opinions about my abilities. Grading – no matter how prescriptive the rubric – is not an objective process. The teachers who liked my work gave me higher marks than those who didn’t, and I learned to gauge what kinds of work they liked by paying attention to what they promoted in their teaching. That didn’t mean I always did what they liked. Sometimes I did what I liked, because my values were different from theirs.
• My university education was not competency-based. It was not about getting qualified to do a job. I know I chose a study path quite different from many of those that need to be ultimately judged to professional standards, like nursing, engineering and accounting. I’m not under the illusion that all university experiences are (or were, in some imagined nostalgic past) like mine. Something I do find troubling, though, is how the assessment security discourse seems to position all university assessment as requiring the same unified treatment (at least, judging by all those appeals about markets crashing and patients being hospitalised because business and medical students used GenAI on their assessments).
• Is this because our leaders imagine that the social sciences, humanities and arts will or should be ejected from university offerings? Or is it because they only studied functionalist disciplines themselves and can imagine no other standard? Or perhaps it’s simply because they need an argument emotive enough to push forward the sales pitch and monopolise all of Australian education with a single approach.
• I could go on about the fallacy of “securing” assessment in the context of profound cultural and economic inequality, sophisticated and advancing wearable technologies, assessor subjectivity, and misalignment with workforce conditions… but today that’s not my point.
• My point is simply that competency-based assessment is not university education. If it were, we should abandon all of these schemes and adopt the rich framework of assessment quality and integrity developed for the Australian vocational education and training (VET) sector, in which nobody needed to be taught about authentic assessment, constructive alignment or validity, because in VET, those are like water to a fish.
• I don’t actually think anyone (and certainly not those pushing the assessment security barrow) want higher education to become VET. Not least because the pay differential is rather punishing. Practice-aligned vocational education is vital, and I am passionate about it – though its governance and execution could be vastly improved – but university’s value is something different.

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• ⁠⁠The most responsible way for colleges to prepare students for the future is to teach AI skills only after building a solid foundation of basic cognitive ability and advanced disciplinary knowledge. The first two to three years of university education should encourage students to develop their minds by wrestling with complex texts, learning how to distill and organize their insights in lucid writing, and absorbing the key ideas and methods of their chosen discipline.⁠⁠
• . ⁠⁠The chemist and philosopher of science Michael Polanyi argued that academic breakthroughs happen only when researchers have patiently struggled to master the skills and knowledge of their disciplines.⁠⁠ “
• ⁠Before embarking on a wholesale transformation, the field of higher education needs to ask itself two questions: What abilities do students need to thrive in a world of automation? And does the incorporation of AI into education actually provide those abilities?
• T⁠⁠he skills needed to thrive in an AI world might counterintuitively be exactly those that the liberal arts have long cultivated. Students must be able to ask AI questions, critically analyze its written responses, identify possible weaknesses or inaccuracies, and integrate new information with existing knowledge.⁠
• rge language model such as ChatGPT: by asking it a question.⁠⁠ What’s a good question? Knowing what to ask and how to ask it is one of the key abilities that professors cultivate in their students. Skilled prompters don’t simply get the machine to supply basic, Wikipedia-level information. Rather, they frame their question so that it elicits information that can inform a solution to a problem, or lead to a deeper grasp of a topic. Skilled questioners rely on their background knowledge of a subject, their sense of how different pieces of a field relate to one another, in order to open up novel connections. The framing of a powerful question involves organizing one’s thoughts and rendering one’s expression lucid and economical.
• ⁠⁠Finally, students must be comfortable and adept at grasping new concepts. This requires a flexible intelligence, driven by curiosity.⁠⁠

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• There's this old adage from economics called Goodhart's law. The econo-nese version of it is that "any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." Or: when a measure becomes a target, it ceases to be a good measure. It could be tweaked to apply to large language models: "when the measure of language becomes its target, it ceases to be good language."
• If we collectively run every bit of text through an AI model to check whether it is AI-generated, we will generate false positives on an even larger scale.

• Ideas don’t come from nowhere, ex nihilo. They don’t pop out of the sky. This is a Platonic vision of the ideal, that the idea sits whole, somewhere outside us, waiting to be apprehended. The reality is that ideas come from a confluence of experiences. Other ideas that morph and transform into some-thing, not a fixed-thing, a momentary-thing that continues to morph and transform itself and others.
• Things that fester. Things that are nourished. Things that percolate in our minds.
• schools see ideas as living on their own, and most often don’t even give them any credence. They don’t think about ideas, because schools assess kids based on what they do with others’ ideas.
• It stops at the operation. It does not delve into the transformation, the metabolic process. It does not value the life that the idea now breathes. It does not ask what festered, what was nourished, what percolated.
• Your ideas are DOA. Dead on assessment.
• Even the word regeneration tells us where this comes from. It is about the ripples that are created, the fact that the work exists well after the work itself.
• Beware the word relationship: a master and a slave have a relationship. The quality of reciprocity is what matters.
• The question is whether the work creates the conditions conducive to life. The totalitarian dictator from the 1930s also moved audiences.
• It is not synthesis, it is metabolism. Transformation that fundamentally transforms who we are, and keeps going because it transforms others and nourishes the confluence. It keeps moving in ways that can’t be known, that can’t be expected or predicted. Transformed and transforming at the same time.

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