The degree must prove more than submission
When generative AI can produce a clean answer outside the classroom, the submitted artifact becomes weak evidence of the student's capability. Live problem-solving, oral defense, source explanation, and observed revision make the reasoning visible again.
Sydney's two-lane model recognizes a necessary tension. Graduates must prove what they can do independently and demonstrate that they can operate in workplaces where AI use will be expected. Either lane without the other leaves the credential incomplete.
Verification costs human time
Oral audits and close observation do not scale as cheaply as automated grading. That is precisely why institutional AI plans must include staffing and assessment redesign rather than promising efficiency while leaving faculty to absorb the verification burden.
The deeper educational purpose is formation, not only answer production. Students develop judgment by struggling with uncertainty, explaining themselves to others, accepting correction, and remaining responsible for the result.
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Deseret News — How AI is changing higher education


