AI in Admissions: Faster Decisions, Better Fit

A public university saved 182 staff hours in a single month by letting AI handle a slice of admissions communications. Another cut a major bottleneck by pairing an AI reader with humans to score hundreds of thousands of essay responses—and aims to send decisions a month earlier. Those aren’t shiny “future of education” promises. They’re operational wins happening right now.

For the Education, Skills, and Workforce Development series, this matters for one reason: admissions is the front door to skills-based education. If the front door is slow, confusing, or inconsistent, you lose people who would’ve become the next cohort of nurses, welders, teachers, cybersecurity analysts, or data techs. Fix admissions, and you don’t just reduce admin stress—you strengthen the pipeline into training and degree pathways that the workforce needs.

AI won’t “replace admissions.” But used well, it reduces wait time, improves consistency, and frees humans for judgment calls—the part that actually shapes class quality and student success.

Why AI in higher ed admissions is taking off

Answer first: AI is spreading in admissions because teams are understaffed, applicant volumes are up, and students expect fast, personalized responses across channels.

Admissions offices have a familiar problem: a mountain of repetitive questions, constant status checks, event scheduling, document reminders, and peak-season surges. Meanwhile, staff turnover is real. When an experienced counselor leaves, the institution doesn’t just lose a person—it loses institutional memory.

AI helps precisely where the workload is most punishing:

• High-frequency questions (deadlines, transcript rules, test-optional details)
• Checklist management (missing documents, next steps)
• Appointment and event scheduling
• Proactive nudges (register for a visit, finish an application, submit FAFSA)
• Peak-period coverage (evenings, weekends, holiday weeks)

There’s also a perception shift happening across higher education. In one widely cited sector survey (Ellucian, 2024), 84% of administrators reported personally using AI, and 93% expected AI use to grow over the next two years. That kind of internal comfort matters—tools don’t scale if leadership treats them like a liability.

Case study #1: SEMO’s “AI teammate” approach to enrollment

Answer first: Southeast Missouri State University shows what AI looks like when it’s treated like staff augmentation—trained, goal-directed, and measured.

Southeast Missouri State University (SEMO) started with an admissions chatbot in 2023. The notable detail: it wasn’t a generic website bot. It was embedded inside the university’s CRM, meaning it could respond based on a student’s record and help complete checklist items. That changes the experience from “here’s a link” to “here’s your next step.”

By 2024, SEMO added AI assistants that tailored guidance based on applicant type (domestic/international, undergraduate/graduate). By 2025, the institution introduced AI agents—a more proactive model.

From reactive support to proactive conversion

SEMO’s agents aren’t waiting for students to ask questions. They’re set up with goals and deadlines, more like an outreach coordinator than a help desk:

• Convert “interested” students into event registrants
• Convert registrants into applicants
• Use multiple channels: email, SMS, webchat, and voice

Here’s the stance I’ll take: proactive outreach is where AI becomes financially meaningful for institutions. Not because it “sells,” but because it reduces the quiet drop-off that happens when students get busy, confused, or anxious and simply stop responding.

The operational payoff: measurable time savings

SEMO reported saving 182 hours in August 2025 by using AI to manage part of student and family communications. That’s not a vanity metric. That’s capacity.

If you’re trying to expand skills-focused programs (short-term credentials, evening cohorts, adult learner pathways), you need admissions capacity that scales without burning out your team.

Case study #2: Virginia Tech uses AI to reduce essay-review gridlock

Answer first: Virginia Tech’s AI essay companion is a model for “human-first, AI-second” review that speeds decisions without removing accountability.

Virginia Tech’s applicant volume surged—from 32,000 (2018) to nearly 58,000 (2024). Growth is a nice problem until it isn’t. Students started complaining about slow final decisions.

The bottleneck wasn’t the whole application. It was essays.

Under the old process, each essay response was read at least twice, sometimes three times, by a trained group of 200–300 volunteer readers. With roughly 500,000 essay question responses, that added up to about 16,000 hours of reading.

Virginia Tech spent three years developing an AI tool with internal research expertise to score essays. The key design choice is the safety mechanism:

1. A human reads and scores the essay.
2. The AI performs the second read.
3. If the AI and human scores differ by more than two points, a second human steps in.

That structure keeps decisions human-owned while still reducing workload. It also creates an audit trail: you can measure divergence, calibrate scoring, and improve training over time.

Virginia Tech’s goal: move final decisions from late February/early March to late January. That’s not just convenience. Earlier decisions help students plan finances, housing, and (crucially) whether they should pursue alternate pathways like community college transfer, apprenticeships, or certificate programs.

What AI changes for workforce readiness (and what it doesn’t)

Answer first: Modernizing admissions with AI improves workforce readiness indirectly—by widening access, reducing friction, and enabling institutions to scale skills-based programs with consistent support.

Admissions isn’t curriculum. It won’t teach someone SQL or HVAC repair. But admissions determines who actually gets through the door, and how quickly.

Here’s the chain reaction I see:

• Faster, clearer admissions → fewer abandoned applications
• More enrolled students (especially adult learners and first-gen students) → stronger talent pipeline
• Counselors freed from repetitive tasks → more time for advising on program fit, credit transfer, and career pathways
• Better program matching → improved retention, fewer major switches, better completion outcomes

If your institution is pushing skills-based education—shorter credentials, stackable certificates, work-integrated learning—AI-enabled admissions can support:

• Evening/weekend recruiting (adult learners don’t apply 9–5)
• Rapid program start dates (rolling admissions need fast processing)
• Better routing (“This applicant is military-affiliated,” “This one needs international document support,” etc.)

What it doesn’t do: it doesn’t magically fix unclear program value, confusing financial aid processes, or weak student support services. If those are broken, AI can scale the confusion.

Guardrails: privacy, FERPA, and the reality of risky conversations

Answer first: AI in admissions only works long-term when privacy controls, identity verification, and moderation workflows are built in from day one.

Admissions touches sensitive data: identity information, transcripts, residency status, disability accommodations, and sometimes mental health disclosures.

SEMO’s approach offers a practical blueprint:

• Email verification for AI interactions to reduce accidental data leakage
• A FERPA-compliant knowledge base
• Encryption and multi-factor authentication for system access
• Third-party audits to validate privacy and security controls

They also dealt with a real-world issue: inappropriate content in an AI conversation. Their response wasn’t hand-wringing; it was engineering and policy:

• Moderation tools that flag conversations
• Rules for escalation (including self-harm signals)
• The ability to block users for violent/discriminatory behavior
• The ability to turn off the AI agent immediately

My opinion: if your vendor can’t support moderation, logging, and rapid shutoff, don’t deploy broadly. Admissions is too sensitive for “we’ll patch it later.”

A practical playbook for implementing AI in admissions

Answer first: Start with low-risk, high-volume tasks, measure outcomes weekly, and keep humans in charge of exceptions and decisions.

If you’re an enrollment leader, CIO, or program director trying to modernize admissions as part of digital learning transformation, this sequence works.

Step 1: Pick two workflows that bleed time

Good starting points:

1. Status checks + missing documents (high volume, low nuance)
2. Event registration + reminders (directly tied to yield)

Avoid starting with tasks that require subjective judgment (e.g., scholarship decisions) until you’ve proven reliability.

Step 2: Build the knowledge base like you’d build curriculum

Treat it as a living asset:

• Write answers in plain language (8th–10th grade readability)
• Create variants for key populations (international, transfer, adult learner)
• Include “what happens next” steps, not just policy excerpts

A snippet-worthy rule: If an AI answer doesn’t reduce the student’s next-step uncertainty, it’s not done.

Step 3: Define human handoffs (and make them fast)

Set triggers that automatically route to staff:

• FAFSA/financial aid distress signals
• Residency/visa complexity
• Accommodation requests
• High-emotion messages (“I’m panicking,” “I’m going to give up”)

Step 4: Measure outcomes that matter for enrollment and equity

Track these weekly during rollout:

• Response time by channel (SMS, email, chat)
• Application completion rate (started vs. submitted)
• Time-to-decision (especially during peak season)
• Escalation rate (how often AI hands off)
• Divergence rate (for essay scoring models)
• After-hours coverage utilization

And don’t skip the human metrics:

• Counselor workload distribution
• Staff retention / burnout indicators
• Student satisfaction from post-interaction surveys

Step 5: Build a governance routine, not a one-time policy

A monthly governance checklist keeps you safe:

• Review flagged conversations and update moderation rules
• Audit knowledge base accuracy (deadlines change constantly)
• Confirm data retention and access controls
• Re-test bias and drift (especially for scoring models)

People also ask: the hard questions about AI in admissions

Will AI make admissions less fair?

It can, if you automate subjective decisions or train models on biased historical outcomes. The safer pattern is what Virginia Tech is doing: AI as a second reader with clear thresholds and human accountability.

Should AI talk to students over SMS and voice?

Yes, if you get consent and set boundaries. Adult learners and working students often prefer SMS. Voice can help accessibility, but it needs strong identity verification and tight scripting.

What’s the fastest ROI for AI in admissions?

Communications and checklist completion. Every incomplete application is a lost opportunity—and often a lost learner who would’ve benefited from skills-focused education.

Where admissions AI goes next

Winter break is a stress test for admissions teams: students are off schedule, families are asking questions, deadlines are looming, and staffing can be thin. AI support during these periods is one of the most practical arguments for adoption.

The bigger story, though, is workforce development. If higher ed wants to be a credible engine for closing skills gaps, it has to run like a modern service organization at the front door—responsive, consistent, and accessible across channels.

If you’re considering AI in admissions, start small, measure aggressively, and keep humans responsible for decisions and exceptions. Then scale the parts that make the student experience calmer and faster.

What would change for your completion rates—and your local talent pipeline—if applicants got clear answers in minutes and decisions weeks earlier?