Every edtech vendor has "AI" in their pitch deck right now. Some of it is transformational. Some of it is a chatbot renamed. Here's a practical guide to separating the two.
Where AI Is Actually Delivering in Admissions
The highest-impact AI applications in higher education admissions right now aren't the flashy ones. They're the unglamorous back-office processes that were previously bottlenecks:
1. Transcript Parsing and Data Extraction
This is the clearest current win for AI in admissions. Processing thousands of transcripts from hundreds of institutions, each with different formats, grading scales, and course naming conventions, is a task that AI handles at accuracy rates (99%+) that rival or exceed manual processing, at a fraction of the time.
The student experience improvement is real: institutions using AI transcript evaluation are responding to applicants in days instead of weeks. In a competitive enrollment environment, that speed advantage is measurable in yield rates.
2. GPA Normalization and Standardization
Comparing a 3.8 on a 4.0 scale to an 88 on a 100-point scale to a 15 on a 20-point French baccalaureate requires systematic knowledge and consistent application. AI handles this more consistently than manual evaluation, and it doesn't have bad days.
3. Predictive Analytics for Enrollment and Retention
Using historical enrollment data to identify which students are at risk of not completing, or which prospects are most likely to enroll given certain interventions, this is an area where AI models are showing real predictive value at scale.
Where to Be Skeptical
Holistic review automation. There's a significant difference between AI helping process data (transcripts, test scores) and AI making admissions decisions. The former is working well. The latter raises serious equity, legal, and accreditation questions that are not yet resolved, and vendors claiming otherwise are ahead of both the research and the regulatory environment.
Chatbots as "AI counselors." Prospective student chatbots can handle FAQ-type queries at scale. But for students navigating complex questions about transfer credit, financial aid, or program requirements, they often create more confusion than they resolve. The ROI on chatbots is frequently overstated.
Anything trained on your student data without explicit consent. This is a FERPA red flag. If a vendor can't clearly articulate whether your student data is used to train their model, ask until you get a direct answer.
The Bias Question
AI bias in admissions is a legitimate concern that deserves direct engagement rather than dismissal. The risk: AI models trained on historical decisions will perpetuate the biases embedded in those decisions.
For document processing (transcript parsing, GPA normalization), this risk is lower - the task is more objective. For predictive scoring that influences admissions outcomes, the risk is higher and requires ongoing auditing.
Any AI vendor in the admissions space should be able to clearly describe their bias testing methodology and provide evidence of outcome auditing. If they can't, that's a significant gap.
A Framework for Evaluating AI Tools
When evaluating an AI tool for admissions or enrollment, ask these questions:
- What specific task does this AI perform? (Be suspicious of vague answers.)
- Where does the human stay in the loop?
- How was the model trained, and on what data?
- Is our institutional data used to train the model?
- How is bias tested and monitored?
- What's the audit trail for decisions made with this tool?
