 CCC Blog Conversations around the impact of artificial intelligence (AI) on the job market have intensified in recent months. With high-profile announcements of layoffs and the increasing visibility of large language models (LLMs) that can generate and explain code, it’s natural that students, parents, and recent graduates are questioning the long-term value of a computer science (CS) degree.
One recent contribution to this conversation comes from Boise State University Associate Professor Casey Kennington and Clinical Instructor Andre Keys, whose recent article draws directly from The Future of Programming in the Age of Large Language Models, a joint whitepaper from the Computing Community Consortium (CCC) and CRA-Industry (CRA-I). Their piece positions the CS degree not as outdated, but as essential to thriving in the AI-powered workforce.
Building on the themes raised in that article — and CCC’s broader work — this blog post outlines several key arguments that academic leaders, faculty, and staff can use when articulating the relevance and enduring value of CS education in the age of AI.
AI Augments, It Shouldn’t Replace
One of the most pervasive misconceptions is that AI tools will simply take over software development tasks entirely, rendering human programmers obsolete. As the CCC/CRA-I whitepaper makes clear, these tools are more likely to assist than replace. Developers are already using LLMs to automate time-consuming tasks such as documentation, test generation, and translation between programming languages — tasks that can enhance productivity but do not displace the need for human judgment.
The core skills of a software developer—understanding complex requirements, analyzing problems, designing robust solutions, and meticulously validating code—remain paramount. AI-generated code still requires human oversight for correctness, security, and readability. An experienced developer’s ability to rapidly read, understand, adapt, and integrate LLM-generated snippets is more critical than ever for deploying, scaling, and maintaining the sophisticated applications that drive modern enterprises.
At the same time, the expansion of AI tools is lowering barriers to entry for many types of software creation. Students and professionals outside traditional tech roles can now more easily prototype tools and applications. However, scaling and sustaining those systems still requires professional software engineering expertise — a role for which CS graduates are uniquely equipped.
A Shifting Market, Not a Shrinking One
Recent tech-sector layoffs have sparked headlines, but they do not tell the full story. Many companies are adjusting hiring levels after pandemic-era growth, not abandoning the need for computing talent. In fact, the demand for professionals with CS training continues to grow across industries — including healthcare, logistics, and precision agriculture — as AI technologies open new possibilities.
What is changing is not the overall need for computing skills, but where and how those skills are applied. Academic leaders are increasingly noting the shift from employment concentrated in large technology firms to more distributed demand across sectors. The future CS workforce will be more interdisciplinary, embedded in organizations seeking to innovate with AI-driven tools and services.
Core Competencies Still Matter
While practical experience with cutting-edge AI tools and frameworks is valuable, it’s the mastery of foundational computer science principles that enables adaptability and long-term success.The ability to “vibe code” with an LLM is insufficient; true professional software development demands rigorous analytical and problem-solving skills to effectively leverage AI’s assistance.
The CCC/CRA-I whitepaper highlights how LLMs are reshaping what is taught — and how it is taught — in introductory programming courses and beyond. For instance, if students use LLMs to write code, instruction must increasingly emphasize reading, testing, and debugging code they did not author.
The whitepaper also underscores the need for students to develop robust mental models of how these tools work. Misunderstanding an LLM’s capabilities or limitations can lead to overconfidence or misuse. A strong CS curriculum can help students learn how to critically engage with AI tools, rather than simply consume their outputs.
Conclusion: A Degree for the Present and Future
As more students arrive on campus with experience using AI tools — or concerns about their future role — CS departments will need to articulate the lasting value of the education they provide. Boise State’s article offers one example of how to do so effectively, grounded in both labor market realities and evolving technical demands.
At CCC, we encourage academic leaders to explore and share The Future of Programming in the Age of Large Language Models, authored by Arjun Guha (Northeastern University) and Ben Zorn (Microsoft). The whitepaper offers insights from educators, developers, and industry leaders on how programming is changing — and how computing education can and should adapt.
You can also browse additional CCC whitepapers for perspectives on computing education, research directions, and the evolving role of the discipline in a rapidly transforming world.
Content mobilized by FeedBlitz RSS Services, the premium FeedBurner alternative. |