Following up with the competitive stance in modern formal education, the prevailing splintered institutional standard is ratified with this proposal submitted to the "Inquiry into the use of generative artificial intelligence in the Australian education system". The Inquiry doesn't appear to have published the submission within their Submissions List, as required; regardless an introduction with the article is herein:
My submission (Jason Jowett) is herein, and stops shorts of blaming the winners of previous education competitions, where intelligence is rated as IQ, and unsubstantiated, rather based on snapshot positioning, which can be considered sorcery furthermore. The argument's that elite winners of educational competition, who achieve entry, and prestige, for social benefit, are actually morally corrupt examples of a covert fascist dictatorship, though this isn't directly emphasized in the submission. A hypothetical prevailing Forth Reich of course will seek to suppress any contradicting case for their supremacy, based ironically on queer elucidation of truth. This is in having position in the winners circle, which requires rejection of those who fail to meet the strict compliance regime. This medieval system prevails ever intrinsically, pitting today's youth against seniors of yesteryear in a hyper-competition spanning industry, business, governments and verifying that weapon production is the pan ultimate success of the corporate vanguard. Using methods such as denial of clarity, with statements like "this doesn't make sense", and then without following up for elucidation, pivots this hostile and morally corrupt vanguard within a protectionist racket. It's one that maintains that protecting their inner circles is for all valid reasons, and nobody has to go out of their way to entertain 'radical propositions'.
Article submitted for The House Standing Committee on Employment, Education and Training who have been asked to inquire into and report on the use of generative artificial intelligence in the Australian education system. Following consideration of:
- The strengths and benefits of generative AI tools for children, students, educators and systems and the ways in which they can be used to improve education outcomes;
- The future impact generative AI tools will have on teaching and assessment practices in all education sectors, the role of educators, and the education workforce generally;
- The risks and challenges presented by generative AI tools, including in ensuring their safe and ethical use and in promoting ongoing academic and research integrity;
- How cohorts of children, students and families experiencing disadvantage can access the benefits of AI;
- International and domestic practices and policies in response to the increased use of generative AI tools in education, including examples of best practice implementation, independent evaluation of outcomes, and lessons applicable to the Australian context; and
- Recommendations to manage the risks, seize the opportunities, and guide the potential development of generative AI tools including in the area of standards.
Generative Artificial Intelligence (AI) in Education, the N.U.S. Case: a new universal
scale
By Jason Jowett
Sources of data applied in research, material design and used in pedagogical methodology are
considered with authenticity in mind. This can be readily and easily advanced by cross-comparing
data-sets, and AI can easily achieve this, making research within a field based on credibility of a new
universal scale.
A new universal scale, if granted, can act to verify data, history, science, news, or anything on a categorical basis. A NUS
can assess value through comparability. That is if information is garnered by a multitude of sources,
cross-cultural sources, primary and secondary, professionally assessed, and thus accredited, that the
‘accreditation’ should better be repurposed as a NUS rating for computational compliance. Let this
NUS, New Universal Scale, compare information in general to scale, and be applied in education. The case to achieve this is
herein discussed.
Education across its instances between Nation-sta
A new universal scale, if granted, can act to verify data, history, science, news, or anything on a categorical basis. A NUS
can assess value through comparability. That is if information is garnered by a multitude of sources,
cross-cultural sources, primary and secondary, professionally assessed, and thus accredited, that the
‘accreditation’ should better be repurposed as a NUS rating for computational compliance. Let this
NUS, New Universal Scale, compare information in general to scale, and be applied in education. The case to achieve this is
herein discussed.
Education across its instances between Nation-states, and States alike, applies a generalized
structural rating to the provision of accuracy in verifietes, and States alike, applies a generalized structural rating to the provision of accuracy in verified data. The Victorian VCE, a general rating,
for example, if at 71, means the student who received this rating is in the top 29% of the State,
regarding accuracy of examinations. As it is the information applied in examinations that is considered
wholly accurate, but in time, changes, ultimately the rating whilst fixed, may in fact reduce. Indeed in
certain sciences, a rating given in 1980, at 99.95, may have reduced considerably by 2023, when
considering the answers given in 1980 to the knowledge bank of 2023. Actually, when would a
graduate in the top 00.05% be reduced to a 29th percentile or lower, on a pro-rata assessment basis?
This doesn’t happen currently as institutional information is diversified, and assessments have no basis
to be reassessed when curricula change. Even if it were possible, a rerating isn’t considered binding
as a University or High School assessment rating is fixed and never changing once issued; the 99.95
VCE student is permanently the best student! Iteration of the usefulness of a generalized educational
rating system emphasizes that the universality of ratings cannot be determined whilst science
particularly, but general knowledge as agreed in academic settings, does indeed change and evolve
as practices improve, and new knowledge is discovered.
The fact that a innovation standard is directly reducing the applicability of a universal rating hasn’t
redress with standard AI such as Chat GPT:
As an AI language model, I don't have direct access to information
about the specific data sets or sources used to train me. I don't
have insight into the specific epochs or data sets that were included
in my training. - ChatGPT
The issue currently pertaining to education hence is that there is no consensus on informational
integrity. There is no rating whereby certain periods of History, or particular topics of Biology, can be rated
as more or less prone to adjustment, and thus ultimately leading to long-term viability for its topical
professionals. Indeed those of certain academic persuasion are required to continually monitor the
situation, upskill, and unlearn proven falsities. So how long can this method be continued for good
societal standing? Moreso, the necessity for a NUS applies to the appropriation of innovation, where
AI can essentially point to gaps in knowledge and hence direct human users to areas within a field
which require address for progressive purposes, so is it fair that when in application of a NUS and
reassessing a 1976 graduate of the top tier percentile, who is now mid-tier; that today’s top tiers
aren’t given priority in one way or another? A reduction in respect for senior professionals may be
the only inherent result here.
The general use of AI has proven a tool for professionals to garner innovation, by simplifying certain
processes. Will AI better direct necessary innovation by properly cataloging its knowledge bank, and
hence act as an impartial director for innovation? Ownership of innovation is then a substantial concern,
but the proper and adequate facilitation of knowledge in general cannot be assumed to continue on a
competitive basis, where those first across the line are rewarded. In other words if education isn’t to
become a sport, a more authoritative rendition of knowledge on the whole is required such as via a
NUS. Thus in returning to the idea that a person is rated on how well they have appropriated
information, the dubious matter of the standing of information in general is outlined, which leads us
to the matter of access, and the inherent competition underway in appropriations of learning and its
orderly access.
To improve educational outcomes hence, the ideal graduate should be notified of redundancies in
information firstly to back trace progress for standing professionals (not rely on their private &
workplaces sourcing of changes to ‘the field’). Secondly, the appropriation of historical assessment
data across all educational institutes, merging the rating systems and compiling these in analysis to
affect a NUS. Thirdly, using the NUS re-engineer the examination system such that predictive
modeling is integrated for alleviation of systematic redundancies, where i.e one year level is taught
something in year 10, then the next year’s year 10 are taught something new, but whilst the new
year 11 are now ill informed. The ultimate outcome will be AI which can account for all possible future
variations or developments such that a career may be properly tailored to fit adaptive sciences, their
knowledge banks and progress achieved with innovation/invention. Once that’s achieved the
education system would follow.
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