If it’s any consolation, this problem of discrepancies in rules is very common at universities now.
I teach at two universities in Japan and occasionally give lectures on AI issues at others, and the consensus I get from the faculty and students I talk with is that there is no consensus about what to do about AI in higher education.
Education in many subjects has been based around students producing some kind of complex output: a written paper, a computer program, a business plan, a musical composition. This has been a good method because, when done well, students could learn and retain more from the process of creating such output than they would from, say, studying for and taking in-class tests. Also, the product often mirrored what the students would be doing in their future lives, so they were learning useful skills as well.
AI throws a huge spanner into that product-based pedagogy, because it allows students to short-cut the creation process and thus learn little or nothing. Also, it is no longer clear how valuable some of those product-creation skills (writing, programming, planning) will be in the years ahead.
And while the fundamental assumptions behind some widely used teaching methods are being overthrown, many educators, students, and administrators remain attached to the traditional ways. That’s not surprising, as AI is so new and advancing so rapidly that it’s very difficult to say with any confidence how education needs to change. But, in my opinion at least, it does need to change at a very fundamental level. That change won’t be easy.