For years, many computer science departments were focused on one challenge: keeping up with booming enrollments. Now the landscape is shifting. Tech hiring has cooled, AI is changing how software gets built, and many of us are asking a different question: What should computer science graduates know how to do that AI can't do for them?
One theme emerging from the conversation is that the value of a CS education may be moving beyond writing code toward designing systems, solving complex problems, and making thoughtful decisions about how technology is used. If AI can generate syntactically correct code, then our students need even stronger skills in architecture, debugging, evaluation, ethics, and communication.
The article cited below also highlights another interesting trend: interdisciplinary computing programs. Rather than expecting students from other disciplines to become full computer science majors, some universities are embedding a strong computing foundation into fields like biology, public policy, and the arts. It's an approach that recognizes that computing is no longer confined to one department—it has become part of nearly every discipline.
For those teaching in higher education, these shifts raise some important questions. How much time should we still devote to syntax and implementation? How can we create learning experiences that require students to explain, critique, collaborate, and design instead of simply producing working code? And what role should AI play in helping students develop those deeper skills?
As the profession and the landscape evolves, our courses need to evolve as well.
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