The assumption most students carry into job interviews about AI is that they need to know more than they probably do.
They imagine employers looking for certifications, courses completed, specific platforms mastered. They worry that they haven't done enough with AI to be competitive. Or they've done a lot with it and wonder how to talk about it without triggering academic integrity concerns. Or they've avoided it entirely because the institutional messaging was negative and they're not sure whether any use was appropriate.
The reality, for most entry-level roles in most industries, is simpler.
Hiring managers evaluating AI fluency in entry-level candidates are not typically looking for technical expertise. According to Kevin Prentiss, Head of Product and Technology at the NSLS, what they're looking for is evidence of engagement, as he discussed during the State of Higher Ed webinar, the kind of genuine curiosity about AI that signals a candidate will continue learning as the tools evolve. Platform expertise is transient. Curiosity compounds.
Understanding what employers actually expect changes how you should be developing (and describing) your AI fluency.
Prentiss described a practical framework for thinking about AI fluency levels during the webinar that helps clarify where the baseline is and what meaningful differentiation looks like.
For most entry-level roles in most industries, demonstrating Level 1 fluency and some meaningful engagement with Level 2 tasks is sufficient to differentiate positively from peers who haven't engaged with AI at all. The bar is not expertise. The bar is genuine engagement.
The differentiating quality that Prentiss identified across both levels is curiosity. Not the word "curiosity" on a resume, but the visible behavior of someone who has actually explored.
A candidate who says "I've been experimenting with AI tools in my coursework; I used it to help analyze data for a research project and I found that it was most useful for the initial pattern identification, but I had to verify all the outputs against the source data" is demonstrating something real: they've engaged, they've reflected, and they understand where the tool is useful and where it isn't.
That answer is worth more to a hiring manager than a certification in a specific platform. It shows that the candidate will continue learning as the tools evolve, because their fluency is built on engagement and judgment rather than on a snapshot of platform competency.
Contrast this with a candidate who says "I have experience with AI" but can't describe specific use cases, can't articulate what they learned, and can't discuss where AI tools are most and least useful in their field. The words are the same; the signal is completely different.
The goal is not to accumulate AI experiences as credential items. It's to develop genuine understanding through deliberate engagement, and then to be able to describe that understanding clearly.
Amy Everson, Senior Director of University Recognition and Institutional Events at the American Public University System, described the model for structured AI development in the State of Higher Ed webinar as a playground with guardrails. Students who engage with AI in structured contexts, where experimentation is encouraged, reflection is built in, and faculty or program guidance provides the guardrails, develop more robust fluency than students who use it informally without reflection.
AI fluency is somewhat universal (basic prompting and critical output evaluation transfer across fields) and somewhat field-specific (the most valuable AI applications vary significantly by industry and role).
Students who demonstrate field-specific AI awareness (who can discuss how AI is being used or is likely to be used in their target industry, and can articulate what skills that creates demand for) stand out significantly from candidates who only know AI in the abstract.
This doesn't require expertise. It requires awareness. Reading industry publications, following what companies in your target sector are actually doing with AI, and having a view on where AI is most and least transformative in your field is accessible to any student willing to spend a few hours on research.
The candidate who can say "In marketing, I see AI being used heavily for content generation and audience segmentation, and I've been experimenting with both, here's what I've learned about where human judgment is still essential" is demonstrating the field awareness that employers in that sector will find directly relevant.
A certification can signal engagement, but it's less important than demonstrated experience. Most hiring managers are more impressed by specific examples of using AI tools thoughtfully than by certificates for courses completed. If a certification is from a credible source and covers content you've actually internalized, it can supplement a genuine experience base. On its own, it's relatively thin.
Focus on what you've learned and how you engaged with it responsibly, rather than whether it was universally permitted. If you used AI tools in contexts where it was allowed (personal projects, co-curricular activities, explicitly permitted assignments) you have genuine experience to discuss. Employers who ask about AI aren't typically concerned about academic contexts; they're asking whether you'll be able to use these tools effectively in a professional environment.
Avoid generic claims ("I'm familiar with AI tools") without specific backup. Avoid implying that you believe AI will do your job for you; that's not what employers want to hear and it's not accurate. And avoid overconfidence about capabilities you haven't actually developed; employers in AI-forward fields will ask follow-up questions.
Be honest and constructive: "I've started experimenting with X and Y, and I'm actively building my fluency. Here's what I've learned so far and where I'm focusing my development." A candidate who is genuinely engaged and building will almost always be preferred over a candidate who claims competency they don't have.
The bar for AI fluency in entry-level hiring is more accessible than most students think, and the differentiator is curiosity, not expertise.
Start engaging deliberately. Reflect on what you learn. Be specific when you talk about it.
For the full data on AI, employer expectations, and career readiness in 2026, read the 2026 State of Higher Ed Report.