I teach courses in discrete mathematics, data structures and algorithms, machine learning, and programming at a mid-tier United States public university. I work with many students: those taking my courses, and teaching assistants. Here are the answers to your questions:
1. The curriculum has not really changed. Some faculty are attempting to use AI in their courses. Most of it is charlatanism, the faculty themselves sort of blundering about using the web interfaces (chatgpt.com, claude.ai). Realistically, most students are not proficient enough to use Claude Code yet.
2. Students are buying into AI behind the backs of faculty. There's something like a consensus among CS faculty that AI ought not be used in introductory courses, other than as a search engine replacement for Q&A. Nonetheless, homework averages now approach 100%, whereas exam averages are falling from B/B- (before AI) to C-/D (after AI). AI use is, for most, obviously undermining foundational learning.
3. The majority of liberal arts courses with substantial enrollments (40+) are referred to by students as "fake," as most of the work for these courses can be completed with AI. Seminars remain robust.
4. Exam cheating is widespread. A cell phone is held in the student's lap. The camera is used to photograph what page of the exam faces down. OCR and AI provide an answer. The student flips the page and copies the answer. I have caught students doing this and awarded them a trip to the dean's office and a course grade of F.
5. It is understood that Grade Point Average (GPA) is not a strong signal of achievement, because for many courses, AI use results in a higher grade (and less understanding). Those who understand more, due to ethical attention to their education, often have lower GPAs than those who engineer high GPAs by taking the easiest, AI-vulnerable courses.
6. Mathematics and theory courses that rely on exams for the overwhelming majority of the grade, and which proctor those exams, retain their rigor and retain their value.
7. Students still land FAANG jobs at a reasonable rate. This school never strongly fed FAANG, and the percentage that attains such a position remains about 10% of graduates. Many other graduates land reasonable positions with startups, financial, automotive, logistics, security, etc. firms.
8. Overall student engagement and give-a-damn is circling the drain. Student routinely perform theatrics, such as responding to in-person class discussions by reading the output of their LLM. Students hauled in to discuss AI use on homework often have scripts prepared: to reveal this, it is a simple matter of forcing the student to deviate from his script.
9. New grad interviews seem to take two flavors: the first flavor is one where the new grad is interviewed by a bot. This is regrettable. The second is whiteboarding how a data structure or algorithm is applied to a specific problem. This is laudable.
But what about uni then?
A. Your nephews should attend to their theory courses heavily and avoid leaning on AI. They will not learn faster with AI use. They will reap benefits from understanding the theory of discrete mathematics, data structures, and algorithms. Even if their future as engineers involves heavy use of AI to generate code, understanding that theory will set them apart from their "peers" rather substantially.