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Most course policies do not give AI a single yes or no. They sort it into three tiers, banned, disclosed, or free, and the tier that actually matters is decided by one line: whether a tool edited what a student already thought, or generated the thinking itself.
A practical walk through what AI use is almost always fine, what is almost never fine, the grey area in between, and how to keep a simple record of what you did, starting from the one policy that actually governs an assignment: the course's own.
Turnitin publishes two false positive figures, not one, and neither describes the paper sitting in front of one particular professor. Here is the document-level versus sentence-level split, the sub-20-percent asterisk, and the arithmetic that turns a fraction of a percent into a real count of students.
ICMJE and COPE both say no, and neither stops there. Their authorship rules explain exactly which two obligations a language model cannot meet, and what happened to the handful of 2023 papers that tried crediting ChatGPT anyway.
Nursing, medicine and allied health programs run academic misconduct and fitness-to-practise as two separate systems, so an integrity finding does not always stop at a grade. Here is how that two-track structure works, and why reflective writing and care plans are exactly where it gets tested.
Vanderbilt, Michigan State and UT Austin have each published their own reasoning for switching off Turnitin's AI writing indicator. None of them dropped Turnitin itself. Here is the distinction, the arithmetic behind it, and which other names get repeated without an institution's own page to confirm them.