Preparing AI Engineers to Responsibly Shape the Future
WGU’s School of Technology is building for change, and our mandate is even more urgent as AI integrates into our personal and professional lives. We’re designing programs to help students develop capabilities and skills they will need now and as technology evolves.
We know that 40% of key workforce skills are expected to change by 2030, according to the World Economic Forum’s Future of Jobs report. AI engineer is the fastest growing AI job this year, according to LinkedIn’s Jobs on the Rise report.
On July 1, we launched the country’s first accredited, competency-based undergraduate AI engineering program, with additional industry-aligned AI programs to be launched in the next year. Already, WGU has been ranked one of the top AI programs in the country. At the same time, the program has received applications from more than a thousand potential students. “Future graduates,” is how I like to refer to these people.
WGU is redesigning how people learn, reskill, and advance their careers. Earlier this month, WGU announced a partnership with Anthropic, an initiative that will help us serve School of Technology students.
I’ve asked WGU’s Jared Plumb, associate dean of AI engineering and software engineering programs, to talk about keeping ahead of technical evolution, the difference between AI literacy and AI engineering and the next AI gap.
Given how AI is advancing so quickly, how can educational institutions design curriculum to remain ahead of technology?
Jared Plumb: The technology is indeed changing rapidly, so chasing every new model or tool simply isn’t sustainable. Instead, institutions should focus on helping students build enduring skills, such as software engineering, systems thinking, problem solving and critical evaluation, while teaching those students how to adapt their skills to emerging AI technologies. The goal is to prepare learners for a career of continuous change.
What makes WGU's Bachelor of Science in AI Engineering different from other programs?
JP: It became clear that AI wasn't going to remain a specialty — it was becoming part of how software is built and how work gets done across nearly every industry. That shift called for something different than another degree about AI or another collection of AI tools. It called for a program that helps students develop the judgment, engineering mindset and sense of responsibility to build systems people can rely on long after today's models and technologies have changed.
Many organizations are adopting AI tools but few are building AI systems. What is the difference between AI literacy and AI engineering and why does that distinction matter?
JP: AI literacy is becoming essential for nearly every profession. It means understanding what AI can do, where it fits and how to use it thoughtfully. AI engineering is different — it's about designing, building, deploying and maintaining systems that incorporate AI. As organizations move beyond experimenting with AI to embedding it in products and operations, they'll need more people who know how to build those systems responsibly.
We often hear that AI will transform work. What new opportunities do you think will emerge?
JP: The job market is already signaling the emergence of roles that don't fit traditional job descriptions. As AI capabilities expand, organizations will need professionals who can bridge software engineering, machine learning, systems integration and AI evaluation. These aren't entirely new disciplines, as much as new combinations of skills that help organizations build trustworthy AI products rather than simply deploy AI tools.
You've said, “The next AI gap is evaluation, not adoption.” What does that mean?
JP: We're reaching a point where access to AI won't be the differentiator because nearly every organization will have it. The competitive advantage will come from knowing how to evaluate AI systems — understanding when they're performing well, where they're failing and how to improve them. The future belongs to organizations that can measure quality and make informed decisions about AI, not just adopt the latest models.
As AI becomes embedded in nearly every industry, what capabilities will be the most valuable for workers over the next decade?
JP: Technical knowledge will be important, but so is judgment. As AI takes on more routine work, people who understand the underlying fundamentals will be best equipped to recognize when AI is wrong, ask better questions and improve the systems they build.
What does responsible AI development look like in practice and what role should engineers play in shaping AI's societal impact?
JP: Responsible AI isn't just about policies or governance; it's about the engineering decisions made every day as AI systems are designed, built, tested and deployed. As AI becomes part of products and business processes, engineers have a responsibility to evaluate how those systems perform, understand their limitations and ensure they're solving the right problems for the people who use them. Strong technical fundamentals and domain expertise matter because AI can't replace human judgment. Building responsible AI ultimately means creating systems that people can trust because they've been thoughtfully engineered, rigorously evaluated and continuously improved.
Learn more about the School of Technology’s AI engineering program here. Discover WGU’s AI skills fundamentals certificate program here, or explore AI concentrations within the computer science or software engineering master’s programs.
If you are an employer, learn more about what's distinctive for the country's leading AI programs and consider partnering with WGU to upskill your employees in the age of AI.
