The Class of 2026 is, by most accounts, the most AI-fluent graduating class in history. They've used AI to write first drafts, debug code (or vibe code), brainstorm ideas, and summarize readings. They've grown up alongside the technology and absorbed it into their workflows naturally, often before their professors figured out the syllabus implications.
They're also, research shows, genuinely anxious about what it means for their futures. Which is fair. I think we all are.
That being said, that anxiety isn't irrational. It points to something real about what's happening in the labor market, and it has significant implications for how career services helps students compete.
The standard narrative is that AI will replace certain tasks while creating demand for others. That's true, but the more important shift for career services is subtler: AI is raising the floor on what employers expect from new hires while simultaneously raising the ceiling on what standout candidates can offer. Students need to learn AI but also need the soft skills and unique projects that make them stand out.
When AI can produce a competent first draft, summarize a report, or parse a dataset in seconds, the skills that distinguish candidates are less about raw technical execution and more about judgment, communication, adaptability, and the ability to work effectively with other people. These are not soft skills in the dismissive sense. They are the hardest skills to fake on a resume and the hardest to teach in a classroom.
NACE's 2026 employer research confirms this directly. Skill-based hiring has become the majority practice, with 70% of employers now using it. And when they evaluate Class of 2026 candidates, what they want to see is demonstrated evidence of competency: not what students know, but what they've done with what they know.
Here's the thing about AI-resistant skills: they're developed through doing, not through being told. You don't build genuine teamwork competency by reading about it. You build it by navigating a real project with real stakes and real personalities. Everyone has done that one group project that taught you way more than you needed to know about group work and communication. (Be honest: Did you just end up doing the whole thing yourself??) Anyway, you don't develop professional communication skills in a vacuum. You develop them through feedback, iteration, and repeated exposure to professional contexts.
That's exactly what experiential learning provides, and it's why the demand for well-structured EL programs is only going to grow.
NACE's own data shows that students embrace hands-on skills development through experiential learning and report that employers are actively helping them build the skills they need. Internships, co-ops, service-learning placements, and applied research experiences give students something no AI tool can generate for them: a real story to tell about real work they did in a real context.
That story is becoming the central currency of early-career hiring.
Career services has always helped students translate experience into opportunity. But the stakes of that translation work have never been higher and the anxiety and apathy of students is becoming harder to combat.
A student who spent a semester in a community development placement has built skills that genuinely differentiate them. But if they can only describe that experience as "I worked with a nonprofit on housing issues," they've left most of the value on the table. The career services team that helps them articulate the specific competencies they developed and connect those competencies to what a particular employer is looking for is doing work that materially changes that student's outcomes.
AI makes this translation work more urgent, not less. Because if AI can generate generic cover letters and polish mediocre resumes, the differentiation comes from specific, credible, human-demonstrated experience that can't be fabricated. Career services is uniquely positioned to help students own that story.
The challenge is doing this at scale. Most institutions are seeing growing student populations, leaner staffing, and more pressure to demonstrate career outcomes... all at the same time. Helping every student articulate the value of their experiential learning, in a personalized and meaningful way, requires infrastructure that doesn't rely entirely on one-on-one advising hours.
That means building systems that capture outcomes throughout the EL experience and not just at the end, so students graduate with a documented record of what they've done and what they've built. It means connecting reflection and competency assessment to the EL process itself, so articulation isn't a last-minute scramble before a career fair. And it means giving career advisors the data they need to have richer, more targeted conversations with students.
The institutions doing this well are the ones sending graduates into the market who don't just have great experiences; They can talk about them. In a world where AI can polish anyone's presentation, specificity and credibility are everything.
Experiential learning, done with intention and the right infrastructure, is how students get both.