AI in Admissions: Faster Decisions, Better Service

Admissions is one of the most “public-facing” services a university runs, yet it often behaves like an old back office: long queues, repeated questions, missing documents, and staff stretched thin. The most useful lesson from recent higher-ed pilots is simple: AI improves admissions when it removes waiting—without removing accountability.

That matters far beyond campus. In this topic series on “አርቲፊሻል ኢንተሊጀንስ በመንግስታዊ አገልግሎቶች ዲጂታላይዜሽን”, we focus on reducing bureaucracy, speeding up service, and making digital support feel human. Admissions is basically a real-world lab for that: high volume, strict privacy rules, and citizens (students and families) who expect answers now.

Two case studies from late 2025 show what “good” looks like: Southeast Missouri State University (SEMO) using AI chatbots, assistants, and proactive agents for end-to-end applicant support, and Virginia Tech using an AI “essay companion” to reduce a massive review bottleneck. Together, they point to a practical blueprint any institution (including government service offices) can borrow.

What AI fixes in admissions (and why it’s not just “automation”)

AI helps most when it shortens the path from intent to action. In admissions, that means moving a student from “curious” to “complete application” to “decision” with fewer delays and fewer handoffs.

The reality? Most admissions delays aren’t caused by one big task. They come from thousands of micro-frictions:

• Applicants ask the same questions repeatedly across email, phone, and social platforms.
• Checklists get stuck because one document is missing, but no one notices quickly.
• Staff turnover creates knowledge gaps and inconsistent messaging.
• Essay review and holistic evaluation don’t scale linearly with applicant volume.

When institutions add AI thoughtfully, they usually target three outcomes:

1. Faster response time (minutes instead of days)
2. Higher completion rates (fewer abandoned applications)
3. More consistent service quality (less dependent on who happens to answer)

For the broader “digital public services” conversation: admissions is a close cousin of passport appointments, benefits eligibility, licensing, and citizen support desks. The same pattern shows up everywhere: people don’t hate procedures—they hate silence, repetition, and uncertainty.

Case study 1: SEMO’s shift from chatbot to proactive AI agents

SEMO’s approach works because it ties AI to real workflows, not just FAQs. They started in 2023 with an AI chatbot embedded in their CRM. That detail is the difference between “nice website feature” and “service delivery tool.”

From answering questions to completing checklist items

A typical admissions chatbot can repeat website information. SEMO’s chatbot did more: it could interact with the applicant’s record (with verification) and help move the application forward.

Examples of tasks AI supported:

• Starting an application
• Checking application status
• Identifying missing documents
• Registering for an event
• Scheduling an appointment
• Sending a congratulatory acceptance message

This matters because the highest-value AI in services isn’t conversational—it’s transactional. It reduces the number of steps a person must take and lowers the cognitive load on staff.

Adding specialization: AI assistants (2024)

In 2024, SEMO introduced AI assistants that curated responses based on applicant category (domestic vs. international, undergraduate vs. graduate). That’s a quiet but powerful move: one-size-fits-all messaging is a major source of confusion in high-volume services.

If you’re designing AI for public-sector digitalization, this is the transferable idea: build policy-aware and user-segment-aware assistance so guidance changes depending on who the user is.

2025’s big shift: goal-based, proactive AI agents

The most forward-looking step SEMO took in 2025 was introducing AI agents that aren’t just reactive. They’re proactive and assigned outcomes with deadlines.

Instead of waiting for a student to ask, the agent can:

• Nudge an interested prospect to register for an event
• Follow up on incomplete checklist items
• Engage across channels (email, SMS, webchat, and even voice)

That’s an operational change, not a tech upgrade. You’re basically turning parts of admissions into an always-on service desk.

Snippet-worthy rule: If your AI can’t trigger the next step (with guardrails), it won’t meaningfully reduce bureaucracy.

A measurable impact: time saved

SEMO reported 182 hours saved in August 2025 by using AI to manage part of student and family admissions communications. That’s a clean signal that the workload wasn’t eliminated—it was redirected. Staff can spend those hours on higher-judgment work: complex cases, counseling, equity review, and outreach that needs a human touch.

Case study 2: Virginia Tech’s AI “essay companion” for scaling review

Virginia Tech’s model is a strong answer to the fear that AI will replace human judgment. Their design keeps the decision with people but uses AI to reduce the time bottleneck.

The bottleneck: 500,000 essay responses

Virginia Tech’s applicant volume grew sharply—from 32,000 (2018) to nearly 58,000 (2024). Essays became the constraint.

Their previous process involved 200–300 trained volunteer readers, with each response read at least twice (sometimes three times). The scale was massive:

• 500,000 essay question responses
• About 16,000 hours of reading

When students complain about slow decisions, this is often why. The institution isn’t idle—it’s overloaded.

The new process: AI as the second read

Virginia Tech built an internal partnership (admissions + academic research in AI/ML) over three years to develop an AI scoring tool.

Their method is structured and defensible:

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

Two points is a design choice that signals seriousness. It also creates an audit trail: you can quantify how often the AI diverges from humans and where.

Virginia Tech’s operational goal is also clear: moving final decisions from late February/early March toward late January.

For organizations digitizing services, the lesson is bigger than essays: AI is most acceptable when it acts as a quality-controlled co-worker, not a black-box judge.

Governance: privacy, security, and “what happens when AI goes wrong?”

AI in admissions touches sensitive personal data, so governance can’t be an afterthought. SEMO’s safeguards show a practical set of controls that apply equally well to government digital services.

Practical guardrails worth copying

SEMO’s approach includes:

• Email verification before AI interactions to reduce accidental data exposure
• A FERPA-compliant knowledge base (in public-sector terms: policy-compliant content management)
• Third-party audits to validate security and privacy standards
• Encryption and multi-factor authentication for system access

That’s the baseline. The more interesting part is how they responded after an incident involving inappropriate content.

Moderation and escalation are non-negotiable

After problematic content appeared in an AI conversation, SEMO requested moderation tools. Now conversations can be flagged instantly, with rules for what happens next.

Examples of actions:

• Flagging self-harm indicators for immediate human review
• Blocking users for violent or discriminatory content
• Turning off an AI agent immediately when needed

This is the public-service takeaway: Digital services need escalation paths, not just chat interfaces. When stakes are high, the system must know when to stop and hand over.

One-liner for leaders: If you can’t pause, audit, and override your AI, you don’t control your service.

A practical blueprint for institutions (and public service teams)

You don’t need “more AI.” You need a map of your service journey and a shortlist of tasks that create queues. Here’s a field-tested way to approach it.

Step 1: Start where the queue is measurable

Good starting points usually have:

• High volume (thousands of similar requests)
• Clear success metrics (time-to-decision, completion rate)
• Repeatable steps (checklists, reminders)

In admissions, that’s status checks, missing documents, event registration, appointment scheduling, and basic eligibility questions.

In government services, it might be application intake, appointment booking, case status, document completeness, and eligibility pre-checks.

Step 2: Use AI for “next-best action,” not vague conversation

Design prompts and agent goals around outcomes:

• “Get the applicant to submit missing transcript”
• “Schedule an advising call”
• “Help an international applicant understand required documents”

This is how you cut bureaucracy: by reducing back-and-forth and preventing dead ends.

Step 3: Put humans where judgment is real

AI is strong at pattern work and fast retrieval. Humans are strong at:

• Exceptions and appeals
• Equity review and contextual evaluation
• Sensitive conversations
• Final decisions

Virginia Tech’s “AI as second read” is a clean model: AI increases throughput while humans retain authority.

Step 4: Build trust with transparency and controls

If you want applicants (or citizens) to accept AI support, your system should be able to answer:

• What data does the AI use?
• Who can access the logs?
• When does it escalate to a human?
• How do we correct wrong guidance?

Trust isn’t branding. It’s process.

What this means for “AI in education and training” (and lead-ready next steps)

Admissions is often treated as separate from teaching and learning, but it’s part of the same pipeline: access. When AI reduces friction at the start, more learners enter programs, more quickly, with better guidance. And the same AI patterns—knowledge bases, agent workflows, moderation, and scoring companions—carry into academic advising, student support, and even staff training.

If you’re planning an AI initiative in an institution or a public office, I’d start with one question: Where do people wait the longest for an answer that could be handled in under two minutes with the right tools? That’s usually your highest-ROI pilot.

The next step is practical: map one service journey, identify the top 10 repeated requests, and decide what must remain human. Then build AI around the workflow, not the other way around.

When you look at SEMO and Virginia Tech together, the message is clear: AI streamlines admissions when it’s integrated, governed, and measured. The same approach can streamline government services too—less bureaucracy, faster decisions, and a digital experience that respects people’s time.