There is a window open right now for students to get ahead of where most employers expect them to be with AI. That window won't stay open.
According to the 2026 State of Higher Ed Report, 40.7% of college students say AI will not affect their career. Another 30.5% say they're only slightly worried about it. The majority of students, in other words, are not engaging with AI fluency as a career priority.
That creates an unusual opportunity for the ones who are.
Kevin Prentiss, Head of Product and Technology at the NSLS, described this moment in the State of Higher Ed webinar: "There is a window in the next six to nine months where students can get ahead of where most employers' expectations are." That window exists because employer expectations for AI fluency in entry-level hires are still forming, still below what they will eventually become, and the students who build even basic fluency now will enter the labor market with a differentiator their peers don't have.
Why Most Students Are Waiting
The distribution of student AI concern (28.8% worried, 40.7% not worried), the rest somewhere in between, reflects a reasonable uncertainty about a technology that is changing quickly and whose implications aren't yet fully visible.
Students aren't wrong to be cautious about overinvesting in a specific platform that may be obsolete by the time they graduate. They aren't wrong to be skeptical of hype. The history of "transformative" technologies is full of predictions that didn't pan out in the ways or on the timelines experts promised.
But the 40.7% who say AI won't affect their career are making a different kind of error, one that's likely to look increasingly costly as they approach graduation. AI is already present in the workflows of most professional environments, in every sector from healthcare to finance to education to creative services. The question is not whether it will arrive. The question is when employer expectations formalize around specific fluency levels, and what happens to candidates who haven't kept pace.
"If you are not worried, you are not paying attention," Prentiss said during the webinar. That's not a statement to stoke fear. It's just a statement about where the labor market is heading.
What Employer Expectations Actually Look Like Right Now
The good news for students who haven't started yet is that employer expectations for AI fluency in entry-level hires are currently modest. The bar is rising, but it hasn't risen as fast as the headlines suggest.
Prentiss described a leveling framework for AI fluency during the webinar. Level 1 is basic prompting: the ability to ask an AI system a coherent question and evaluate whether the output is useful. Most students who have used ChatGPT, Claude, or any similar tool are already at Level 1, whether or not they've thought about it as a professional skill.
Level 2 is more sophisticated: the ability to put data in and get a meaningfully different, remixed output, creating a first draft from raw material, summarizing a complex document, generating options from constraints. This level requires more intentional practice, but it's accessible to any student willing to spend a few hours experimenting.
What matters at both levels, and what Prentiss identified as the differentiating quality, is not platform mastery but curiosity. Hiring managers who evaluate AI fluency aren't looking for students who have memorized the capabilities of a specific tool. They're looking for students who have demonstrated that they engage with AI actively, experiment with it deliberately, and think about how it applies in their field. Curiosity is visible. And it compounds: each experiment builds familiarity that makes the next experiment more productive.
How to Start and What Not to Do
The students who build AI fluency most effectively aren't spending hours on AI courses or certifications. They're applying AI tools to work they're already doing.
Here's what that looks like in practice:
Use AI to help with real work: Writing a research paper? Use AI to generate an outline from your notes, then evaluate what it gets right and wrong. Working on a project with ambiguous requirements? Use AI to generate multiple interpretations, then use your judgment to select the most useful one. The learning happens in the evaluation and judgment step, not in the generation step.
Experiment across use cases: The fluency that matters to employers is general: the ability to apply AI tools in new contexts, not just in the one context where you've practiced. Deliberately applying AI to different kinds of tasks, from research, writing, analysis, project to planning, builds the flexible fluency that translates across jobs.
Reflect on what you learn: Students who can describe what they tried, what worked, and why have a clear narrative for interviews: "I've been experimenting with AI tools in my coursework and here's what I've learned about where they're most and least useful in my field." That's a differentiating answer.
Amy Everson, Senior Director of University Recognition and Institutional Events at the American Public University System, described the model institutions should be building in the State of Higher Ed webinar: a playground with guardrails, structured experimentation where students can explore AI capabilities with faculty guidance, rather than being either discouraged from using it or left to use it without reflection.
Frequently Asked Questions
Do I need a technical background to develop AI fluency?
No. The fluency employers are looking for at the entry level doesn't require coding, data science, or technical skills. It requires the ability to use AI tools effectively, evaluate their outputs critically, and apply them to real work. Those are cognitive skills that any field develops.
What if the AI tools available now are obsolete by the time I graduate?
The specific platforms will change; they already are. The underlying skill being developed isn't platform-specific; it's the ability to work with AI tools in a professional context, evaluate outputs critically, and adapt to new tools quickly. That meta-skill transfers regardless of which platforms are current when you graduate.
How do I talk about AI fluency in a job application?
Describe specific use cases and what you learned. "I used AI tools to assist with X, and I found that they were most useful for Y and less reliable for Z" demonstrates genuine engagement far more effectively than "I have experience with AI." Specific, reflective examples signal the curious engagement that employers are actually looking for.
What if my professors or institution discourage AI use?
This is a real tension. The most useful approach is to be transparent with faculty about AI use when it's relevant, understand your institution's policies, and find contexts where experimentation is appropriate (personal projects, career development activities, co-curricular programs). The goal is to build genuine understanding of AI capabilities and limitations, not to circumvent academic policies.
Most students aren't thinking seriously about AI fluency yet. That's the window. The students who start now, those who build even basic fluency through deliberate experimentation, will enter the job market with a differentiator that compounds with every month of practice. For the full data on AI, career readiness, and what positions students for success in 2026, read the State of Higher Ed Report.
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