In a recent Communications of the ACM article titled Whither Computing (June 26, 2026), Moshe Y. Vardi argues that computing is at a major crossroads due to the rapid rise of generative AI and AI coding tools. He suggests it’s time to rethink how we approach computing education, research, and careers in an AI-driven world.

One point that resonated with me is Vardi’s observation that many computer science and software engineering students are uncertain about their future. Concerns about fewer entry-level jobs, reduced research funding, and slower hiring have become increasingly common. At the same time, AI-powered coding assistants are changing how software is developed, raising questions about traditional pathways for junior developers and the skills future graduates will need.

Vardi also notes that AI is reshaping computing education itself. If AI can generate code and complete many assignments, universities may need to reconsider what they teach, how they assess learning, and which skills should remain at the core of computing programs. I am really struggling with how to navigate this rapidly unfolding new world

Beyond education and employment, the technology sector is facing growing public skepticism about AI and its societal impacts. According to Vardi, this creates a new kind of “image crisis” for computing, one that extends beyond economic concerns and challenges how the public perceives the profession.

The key takeaway is that generative AI may represent a long-term transformation, not a temporary disruption. Rather than assuming the field will adapt as it has in previous technology cycles, Vardi argues that educators, researchers, professional organizations, and students need to have serious conversations about computing’s future.

Some of the questions he raises include:

• Should programming remain the central focus of computer science education?
• How should universities assess learning when AI can complete many traditional assignments?
• What human skills will distinguish computing professionals in an AI-assisted workplace?
• How can programs prepare students for a labour market where some entry-level coding tasks are increasingly automated?

Vardi does not offer definitive answers, but he makes a compelling case that the computing community needs to start addressing these questions now. I happen to (cautiously) agree.

Connected to this ongoing conversation is a May 2026 article by Esther Shein in Communications of the ACM titled Protecting Higher Education in the Age of AI. The article explores a question many of us in higher education wrestle with every day:

Are students truly learning, or are they relying on AI to do the work for them?

It’s a great question because, at this point, the issue is no longer whether students are using AI. Hey, guess what, most are. Tools like ChatGPT, Copilot, and Claude have become part of the academic landscape, helping students write, code, summarize information, and solve problems in seconds. As the article states, the challenge is figuring out what role these tools should play in learning.

Shein highlights an important concern raised by many educators, myself included: learning is often messy, frustrating, and difficult. Real understanding comes from grappling with concepts, making mistakes, receiving feedback, and trying again. When students rely too heavily on AI-generated answers, they may complete assignments successfully without developing the underlying knowledge, critical thinking skills, or problem-solving abilities that those assignments were designed to build. Reflecting on this, I don’t think this is new to AI use in academia, particularly in Science and Engineering, where some students simply want to be told what to do and follow a straight line to success (i.e. a successful grade). In my opinion, this isn’t learning, and we educators have possibly been failing at this for years!

Regardless, this concern is still especially relevant in computing and engineering programs. If an AI system can generate functional code within seconds, how do we know students understand what the code is doing? More importantly, how do we ensure they can solve problems independently when AI assistance is not available? Are you comfortable flying in an airplane or, more realistically, using tax software where most, if not all, of the code was AI-generated? Not me!

Shein discusses how institutions such as Tel Aviv University and Carnegie Mellon University are focusing instead on transparency, clearly communicating when and how students can use AI in a course. I’ve tried similar approaches in my own courses. That said, I’m still not convinced we’ve figured out whether these policies create meaningful educational value or simply make us feel more comfortable about a rapidly changing situation.

What strikes me most is that this debate is not really about technology. It’s about learning. It’s the whole point, after all!

AI is undoubtedly useful. My students use it, and hey, I do too! I use it to brainstorm ideas and review drafts. Used thoughtfully, AI can be an incredible partner. I’ve seen it firsthand. It can provide instant feedback, explain unfamiliar concepts, and help overcome brain blocks. But there is a difference between using AI to support learning and using AI to replace learning. I definitely fear the latter. The hard part is figuring out where that line is. When does AI act as a tutor? When does it become a crutch? When does efficiency come at the expense of understanding?

Shein argues that higher education needs to move beyond debates about whether AI belongs in the classroom. It is already here. Instead, universities should focus on helping students develop both AI literacy and the uniquely human skills that remain essential: critical thinking, creativity, communication, ethical judgment, and the ability to navigate complexity.

Perhaps that’s the right path forward. Or perhaps we’re still asking the wrong questions. Man—I just don’t know!

Either way, the conversation feels bigger than AI itself. It is forcing us to reconsider what learning means, what skills matter most, and how education should evolve in a world where access to information and expertise is increasingly automated. And hey, that’s a great thing regardless of my own feelings of, sometimes, impending doom…haha.

What a strange, exciting, maybe even scary time to be alive.

-tMac

Featured Photo by Nahrizul Kadri on Unsplash