The Career Sip: Your Weekly Dose of Higher Ed Hustle!

Welcome back to your weekly dose of higher ed hustle, where career development meets caffeine and clarity.

This week’s Career Sip is packed with sharp strategies, smart ideas, and a fun fact that might just steal the show.

Stay with us, there’s something worth sipping all the way through.

Latest in Career Development

Coaching Students for the AI Interview Era

Remember when the biggest interview hurdle was figuring out how to make eye contact on Zoom without staring at your own face the whole time? That was phase one. 

Now we’re in phase two: AI-assisted interviews, where students aren’t speaking to a human at all, but to a platform that evaluates their content, delivery, pacing, and sometimes even their facial expression.

Pre-recorded, AI-assisted interviews are no longer a novelty. They’ve become a standard first-round screening tool across industries. Candidates receive questions on screen, record their answers, sometimes get limited practice attempts, and then submit their final take. The system evaluates how well they connect their experience to required competencies, analyzes their clarity and confidence, and generates a score or summary for recruiters. In some cases, the AI is even programmed to ask follow-up questions if answers feel vague.

For students, this can feel deeply uncomfortable. There’s no nodding hiring manager. No encouraging smile. No clarifying question in real time. Just a blinking “RECORD” button and a countdown clock.

And that’s exactly where career advisors come in.

The fundamentals haven’t changed. Strong interviews still come down to preparation, clarity, and alignment with the job description. But the environment has changed, which means our coaching must evolve with it. Students need help understanding that AI-assisted interviews are still competency-based. The platform is scanning for evidence of leadership, collaboration, problem-solving, adaptability. If they don’t explicitly connect their story to the skill being evaluated, the system can’t “infer” it the way a human might.

That means we should be doubling down on helping students dissect job descriptions with precision. Instead of generic interview prep, we can teach them to reverse-engineer likely questions from listed responsibilities and required skills. If a posting emphasizes team leadership and policy interpretation, students should be prepared with structured, specific stories that demonstrate exactly that. Vague anecdotes will not survive an algorithm trained to score against defined competencies.

Delivery matters more than ever, too. AI platforms are often trained to assess pacing, vocal variety, and clarity. Students who rush, trail off at the end of sentences, or rely heavily on filler words may be penalized without even realizing it. Encouraging students to record themselves during mock interviews and watch the playback can be transformative. Not to obsess over appearance, but to build awareness. How do they sound? Do they end their stories with clear impact? Do they articulate results, not just actions?

We also need to help students think about environment as strategy. AI-assisted interviews are highly sensitive to lighting, background noise, camera positioning, and technical glitches. A poorly angled webcam or echoing microphone can subtly undermine confidence cues. Coaching students on setting up a clean, well-lit space and doing a tech rehearsal is no longer “extra credit” advice. It’s baseline readiness.

Perhaps most importantly, we can normalize the awkwardness. Almost no one loves interviewing, and speaking into a laptop without feedback can heighten anxiety. But preparation reduces fear. When students understand that the system is evaluating structure, relevance, and clarity (not perfection) they can shift from performance mode to storytelling mode.

This is also a moment to integrate AI literacy into interview prep. Students should understand how these systems work at a basic level. Not to game them, but to communicate effectively within them. When they know the platform is scoring alignment to skills, they become more intentional about explicitly naming competencies and outcomes. When they understand that follow-up questions may trigger if answers lack specificity, they learn to include measurable results from the start.

AI-assisted interviews aren’t replacing human judgment entirely. They’re filtering the pipeline. Our role is to ensure students make it through that filter.

The students who will thrive in this new landscape won’t just be confident. 

They’ll be prepared, structured, and self-aware. And career advisors who adapt their coaching now will give them a serious edge before they ever hit “RECORD.”

Books you can't miss

What are we reading

This week, we’re stepping slightly outside the career services playbook and into the automotive industry.

We’re reading Inevitable: Inside the Messy, Unstoppable Transition to Electric Vehicles by Mike Colias, and yes, it’s about electric vehicles. Stay with us.

Colias unpacks the turbulent, political, capital-intensive shift from internal combustion engines to EVs, showing how legacy automakers are forced to make uncomfortable, long-term bets before the market fully tips. The thesis? Technology and economics eventually win. But only for the organizations bold enough to invest early and absorb short-term friction.

So what does this have to do with career services?

Everything.

Think of the analogy this way: fully residential degrees are like gas-powered cars. Hybrid degrees are, well, hybrids. And fully online degrees? That’s your EV transition. Whether we’re personally nostalgic for campus-only models or not, enrollment data tells a clear story: online and hybrid learning aren’t side projects anymore. They’re core infrastructure.

For career advisors, this shift has real implications. More online learners means different engagement models, different internship pipelines, different employer expectations, and different support touchpoints. If institutions need to build “online muscle” over years, so do career centers. Virtual employer events. Remote internship strategies. Scalable coaching. Data-informed student outreach.

The most powerful insight from Inevitable isn’t about cars. It’s about leadership under uncertainty. Legacy automakers had to decide whether to protect what worked or invest in what’s coming. Universities (and by extension, career services teams) face a similar choice.

If your institution is moving toward more flexible, online, or hybrid pathways, how is your career strategy evolving alongside it?

Sometimes the best way to understand higher ed’s future… is to read about batteries.

Fun

Meme of the Week

Latest in Tech

AI at Work in Higher Ed: Adoption Is Breaking Records, Governance Is Not

If you needed proof that AI is no longer “emerging” in higher ed… here it is. A new 2026 report from EDUCAUSE, The Impact of AI on Work in Higher Education, shows record-level adoption:

👉 94% of higher ed employees say they’ve used AI for work in the past six months.

Yes. Ninety-four percent. And yet… only 54% know whether their institution has policies governing AI use.

The survey (1,960 professionals across public and private institutions) paints a very clear picture:

• 56% are using AI tools not provided by their institution
• Only 11% are required to use AI for work
• 38% use AI daily, 34% weekly
• 54% used AI for 8+ different work tasks in six months

Most common uses?

• Brainstorming (63%)
• Drafting emails (62%)
• Summarizing documents or meetings (61%)
• Proofreading (56%)
• Creating presentations (47%)

Faculty are using AI heavily for learning design (63% for assessments and activities).

But here’s the real tension:

Even among executives, managers, and tech leaders, a large percentage don’t know whether AI policies exist. That suggests governance hasn’t just failed to communicate, in many cases, it simply hasn’t caught up.

If 94% of staff are already using AI informally, your students absolutely are too.

And the risks listed in the report (misinformation, data misuse, loss of critical thinking) are the exact skills employers are already asking about in interviews.

But so are the opportunities:

• 70% see automation of repetitive tasks as a major upside
• 65% see AI reducing administrative burden
• 60% see value in analyzing large datasets

For career advisors, this is the moment to:

1. Move beyond “Can students use AI?” Start asking: Can they use it responsibly, strategically, and ethically?
2. Integrate AI literacy into career prep. Resume drafting with AI. Interview simulations. Industry research. Prompt engineering as a professional skill.
3. Model best practice. If staff adoption is this high, career centers can lead by example — documenting how AI is used, where guardrails exist, and what ethical use looks like.

Interestingly, 92% of institutions say they have AI strategies, but only 13% are measuring ROI. That’s a gap career teams can help close. If AI reduces advising time, increases workshop attendance, or improves application quality, measure it. Own the narrative.

AI adoption in higher ed isn’t coming. It’s already here, at record levels.

The real question isn’t whether AI belongs in career development. It’s whether career services will help define how it’s used, or simply react to it.

You can read the full report here: 🔗 https://www.educause.edu/research/2026/the-impact-of-ai-on-work-in-higher-education

Learn something new

The Fun Fact of the Week

There’s a clock being built inside a mountain in Texas designed to tick once a year.

Not once a second. Once. A. Year.

It’s called the “Clock of the Long Now,” and it’s engineered to keep time for 10,000 years. The idea? To encourage long-term thinking in a world obsessed with quarterly results and next-week deadlines.

Here’s the fun part: the clock’s chime will be different every single day for those 10,000 years. That’s over 3.6 million unique chimes. No repeats. Ever.

Why is this oddly inspiring for those of us in higher ed? Because career development is long-now work. The workshop you run this semester, the micro-internship you help design, the AI policy you draft, their real impact might unfold years (or decades) from now.

So next time you’re in an elevator and someone asks what you do, you can say:

“I basically help students think in 10,000-year clock timelines… but with better Wi-Fi.”

Long-term thinking never goes out of style.