AI Is Reading College Applications Too: What Students Should Know About Automated Admissions Review
A student spends four months writing a college application.
She checks every course title against her transcript. She rewrites her activities section until “helped with robotics club” finally becomes something concrete. She removes a sentence from her personal statement because it sounds suspiciously like a TED Talk. Her counselor reads the application. Her mother reads it. Her English teacher reads it and circles the same comma three times.
Then the application enters a university database.
A computer may classify the transcript. Another system may calculate academic rigor. Software may identify missing materials, summarize a recommendation letter, flag an inconsistency, estimate enrollment likelihood, or produce data points about the student’s writing.
At a growing number of institutions, artificial intelligence is no longer standing outside the admissions office, tapping politely on the glass.
It is already inside.
That does not mean a robot is secretly selecting the incoming class. Publicly documented systems are generally described as tools that assist admissions employees, automate administrative work, or provide information for human review. Virginia Tech, for example, says its AI essay reader confirms a human reader’s scores rather than making admissions decisions. The University of North Carolina at Chapel Hill says it uses AI to produce data points about Common App essays and school transcripts while trained people still evaluate applications comprehensively. (Virginia Tech’s official announcement; UNC Undergraduate Admissions AI FAQ) (Virginia Tech News)
Still, this is a meaningful change.
Applicants have spent several years debating whether students may use AI to write application essays. The newer question is just as important:
How may colleges use AI to read, organize, interpret, summarize, score, or verify the materials students submit?
The answer is complicated because different institutions use different systems, many colleges disclose very little, and the phrase “AI in admissions” can describe everything from harmless document sorting to high-stakes predictive scoring.
This article separates what is documented from what is possible, what is possible from what is speculative, and useful preparation from pointless attempts to impress an algorithm no applicant can see.
The Direct Answer: Are Colleges Using AI to Read Applications?
Yes, some colleges are publicly using artificial intelligence in parts of the undergraduate admissions process.
Documented or commercially available uses include:
Extracting courses and grades from transcripts
Calculating or describing curriculum rigor
Identifying missing or duplicate documents
Summarizing essays and recommendation letters
Producing data points about writing style and grammar
Scoring short-answer essays against a rubric
Comparing an AI score with a human reviewer’s score
Supporting research-authenticity verification
Organizing applications into review groups
Predicting admission, enrollment, yield, or student success
Automating communications and administrative workflows
These functions are not equivalent.
A system that converts a transcript PDF into structured course data is performing a different task from a model that assigns an essay score. A tool that summarizes a recommendation is different from a tool that predicts whether the applicant should be admitted. A model used for research is not necessarily deployed in an actual admissions cycle.
The most responsible way to discuss AI in admissions is therefore to ask four questions:
What task is the system performing?
What information does it analyze?
How does a human use the output?
Can the system’s error meaningfully affect an applicant?
Without those answers, the statement “the college uses AI” tells us surprisingly little.
What “AI Reading an Application” Can Actually Mean
Imagine the application as a stack of documents arriving at a busy office.
Before anyone debates whether the student would contribute to campus life, the institution must receive the files, associate them with the correct applicant, interpret different transcript formats, verify completion, route materials to readers, and organize tens of thousands of records.
AI can enter at nearly every stage.
AI May Process the Application Before a Reader Sees It
Applications arrive from different schools, countries, counselors, platforms, and testing organizations. Transcripts may contain different grading scales, course abbreviations, semester structures, class ranks, weights, and notations.
Automated transcript-processing systems can extract information from uploaded records and convert it into structured data. Common App announced in October 2025 that it was partnering with EdVisorly to pilot AI-powered transcript processing in Texas, describing the initiative as an effort to reduce application friction, assist admissions teams, and improve access for lower- and middle-income students. (Common App and EdVisorly announcement) (Common App)
This may involve identifying:
Course titles
Grade levels
Semester or trimester grades
Credits attempted
Credits earned
GPA information
Class rank
Advanced-course labels
Repeated courses
Withdrawals
Subject-area sequences
School grading scales
Automating this work can reduce repetitive data entry. It can also introduce errors if a model misreads an abbreviation, treats a local course designation incorrectly, or misunderstands an unusual transcript format.
The human stakes depend on what happens next.
If an employee verifies every extracted value against the original transcript, the AI is functioning largely as an administrative assistant. If the extracted data flows automatically into academic calculations without meaningful validation, an error becomes more consequential.
AI May Analyze Academic Rigor
UNC states that its AI programs provide data points about the rigor of a student’s coursework, along with information about grades, writing style, and grammar. UNC explains that the purpose is to allow its admissions team to focus on the content of essays, grades, and the extent to which students challenged themselves academically. (UNC Undergraduate Admissions AI FAQ) (Undergraduate Admissions)
“Course rigor” sounds simple until someone tries to calculate it.
Is AP Statistics more rigorous than honors calculus?
What does “Advanced Topics in Scientific Modeling” mean at a rural high school with no AP program?
Is a dual-enrollment English course taught at the high school equivalent to the same course taught on a college campus?
How should a system interpret Cambridge, IB, A-Level, French Baccalaureate, Indian board, homeschool, Montessori, competency-based, block-schedule, or narrative-evaluation records?
Human admissions readers rely on school profiles, counselor explanations, institutional knowledge, and local context to answer these questions. An AI model can identify patterns, but the usefulness of those patterns depends on the quality of its training data, the accuracy of the school information, and whether human reviewers understand the model’s limits.
A course name is not the course.
“Physics” may mean a conceptual ninth-grade class, an advanced calculus-based course, or something in between. A responsible review process must avoid treating labels as complete descriptions.
AI May Summarize Essays and Recommendations
Technolutions, the company behind the Slate admissions and enrollment platform, advertises an “AI Reader” feature that can summarize documents under review, including college essays and letters of recommendation. The platform says institutions can provide directives that tailor these pre-reads to particular review goals. (Slate AI Reader) (Technolutions)
This is evidence that the capability is commercially available.
It is not evidence that every institution using Slate has enabled the feature, that every document is summarized, or that an AI summary replaces a complete reading. Applicants should be careful not to turn a vendor feature into a universal claim about college admissions.
Still, the possibility raises an important issue.
A recommendation letter may contain a subtle comparison:
Maya was not initially the strongest student in the class. What distinguished her was the way she responded when the material became difficult.
A poor summary might reduce that to:
Student initially struggled but demonstrated perseverance.
Technically accurate. Emotionally flattened.
The original letter contains tension, chronology, and judgment. The summary contains a trait.
That is the central danger of summarization in holistic review: the compression may preserve facts while losing meaning.
AI May Score Essays Against a Rubric
Virginia Tech publicly announced that it would use AI to help review its short-answer essays beginning with the 2025–2026 admissions cycle. The university emphasized that the system confirms human essay scores and does not make admissions decisions. Virginia Tech said the change would allow essays to be reviewed more quickly and consistently and would support earlier admissions decisions. (Virginia Tech admissions-process update; Virginia Tech admissions FAQ) (Virginia Tech News)
The Associated Press reported additional operational details: one human reader and the AI system score the short responses, and another person becomes involved when the scores differ by more than two points on a twelve-point scale. The university described the model as trained on previous applicant essays and the institution’s scoring rubric. (Associated Press investigation into AI in admissions) (AP News)
This is not the same as feeding an essay into a public chatbot and asking, “Would you admit this person?”
A rubric-based system is trained or configured to identify particular features and produce a score within a defined process. The consequential questions become:
What does the rubric measure?
How representative were the training essays?
Were past human scores consistent enough to serve as labels?
How often does the AI disagree with readers?
Does disagreement vary by language background or student population?
What happens when a response is unusual but effective?
Are the model and rubric updated when prompts change?
Does a human read every response in full?
How are errors audited?
The phrase “human in the loop” is reassuring only when the human has time, authority, information, and a meaningful opportunity to disagree.
AI May Evaluate Writing Style and Grammar
UNC says its AI tools provide data points about writing style and grammar in Common App essays. The university states that these data points help its admissions team focus on essay content and the student’s academic record. (UNC Undergraduate Admissions AI FAQ) (Undergraduate Admissions)
That public explanation leaves several questions unanswered. It does not specify on the FAQ page which model is used, how the data points are calculated, how much weight they receive, how error rates are tested, or whether subgroup performance is audited.
The absence of those details does not prove the process is unfair.
It means an applicant cannot confidently reverse-engineer it.
This is important because students are extraordinarily talented at inventing rules from incomplete information. One person posts that contractions appear “more human.” Another says shorter sentences receive higher scores. Someone else recommends including the word “leadership” three times.
Soon an entire mythology appears.
There is no sound basis for writing that way.
An essay should communicate something meaningful to the human admissions officers who remain responsible for evaluating the application. Trying to manipulate an undisclosed language model can make the writing less natural, less precise, and less memorable.
AI May Help Verify Research or Other Claimed Work
The Associated Press reported that Caltech introduced an AI-based process to examine the authenticity of research submitted with applications. According to the report, applicants may upload research and respond to questions through an AI chatbot, with the resulting video reviewed by faculty. (Associated Press investigation) (AP News)
Caltech’s official research-submission requirements already ask applicants to describe how they obtained the opportunity, their role, the time frame, the research topic, and the paper’s status. Caltech also requires an additional recommendation from someone who can discuss the applicant’s specific contribution to the research. (Caltech research-paper submission requirements) (Undergraduate Admissions)
That combination illustrates a broader admissions trend.
As student profiles become more polished and outside assistance becomes more sophisticated, institutions may seek evidence not just that a project exists but that the student understands it.
A student who genuinely performed the work should be able to explain:
The original research question
The student’s individual role
The methods used
The most important limitation
What failed
What the student would change
Which sections the student wrote
How collaborators divided the work
What the results do and do not establish
Students should not prepare by memorizing technical language.
They should prepare by actually understanding the work they claim.
AI May Predict More Than the Application Directly States
Admissions models do not need an explicit field labeled “wealth,” “gender,” “school resources,” or “family background” to detect patterns associated with those characteristics.
Text itself can contain proxies.
A study analyzing 283,676 application essays found that a relatively interpretable classifier could predict gender and household income from essay content with substantial accuracy. The researchers argued that computational auditing could help identify bias in both human and machine review. (AI and Holistic Review: Informing Human Reading in College Admissions) (arXiv)
This finding is not proof that admissions offices use essays to infer household income.
It demonstrates that writing can carry socioeconomic and demographic information even when an institution does not explicitly ask a model to find it.
An applicant writing about international travel, unpaid laboratory work, family caregiving, a neighborhood job, religious practice, immigration, transportation barriers, or household responsibilities reveals context through subject matter and language. A machine-learning model can detect relationships that no one deliberately programmed as a rule.
That is one reason “remove the protected variable” is not a complete solution to algorithmic bias.
The remaining variables may still act as proxies.
Research Is Testing AI-Based Holistic Review
Several academic studies have examined how machine learning might support or audit admissions review. These studies should not be confused with confirmed institutional practice, but they reveal what is technically possible.
Models Can Extract Personal Qualities From Essays
A 2023 study published in Science Advances examined whether language models could identify seven personal qualities in short application essays: prosocial purpose, leadership, teamwork, learning, perseverance, intrinsic motivation, and goal pursuit.
Researchers first obtained human ratings for 3,131 essays and used them to train language models. They then applied the models to a national sample of 309,594 applications. The computer-generated scores added some predictive information about six-year college graduation, and the researchers reported evidence that the models reproduced human codes across demographic subgroups in the studied data. (Using Artificial Intelligence to Assess Personal Qualities in College Admissions) (PMC)
That result is interesting. It is not a command for students to insert obvious references to perseverance and teamwork into every paragraph.
The authors themselves noted a foreseeable concern: once applicants know what an algorithm is seeking, they may alter their essays to produce desired signals. (PMC)
This is sometimes called gaming the metric.
Once “leadership” becomes a detectable feature, students may describe every activity as leadership. Once “prosocial purpose” becomes desirable, every project suddenly exists to serve humanity. The application becomes louder but not more truthful.
A model may detect the words.
A good admissions officer may detect the performance.
Models Can Predict Past Admissions Decisions
Researchers have also tested machine-learning models trained on previous admissions outcomes.
One study involving 13,248 applications examined whether a learned model could help create subsets of applicants for human review in a test-optional environment. The model outperformed an SAT-based sorting method in the studied setting and approximately matched the demographic composition of the prior admitted class. The researchers framed the system as a possible support for human decision-making, while discussing the risks of deployment. (Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions) (arXiv)
Another study used data from 14,915 applicants at a selective institution to examine whether text from essays and recommendations could help a model predict admissions outcomes when protected attributes were removed. Text partially restored predictive performance, but it did not preserve the same representation of underrepresented minority applicants. (Augmenting Holistic Review in University Admission Using Natural Language Processing) (arXiv)
This reveals a fundamental problem.
A model trained to reproduce past decisions may learn the institution’s prior judgment patterns—including inconsistencies, preferences, institutional priorities, and inequities embedded in those decisions.
It can become very good at recreating yesterday.
That is not necessarily the same as making tomorrow fair.
The Difference Between Predicting Admission and Predicting Success
People frequently blur two different questions:
Would this institution have admitted the applicant in the past?
Is this student likely to succeed in college?
Those are not interchangeable.
A model predicting past admission decisions learns the behavior of a selection system. A model predicting graduation or first-year performance learns relationships between applicant data and a later outcome.
Both involve value judgments.
How should “success” be defined?
First-year GPA?
Four-year graduation?
Six-year graduation?
Retention?
Research participation?
Employment?
Community contribution?
Student well-being?
Graduation after transferring?
Success despite needing additional support?
A college that predicts who will graduate most easily might favor applicants who already possess substantial resources. A mission-driven institution may instead want to identify students with high potential who would benefit significantly from support.
Prediction is not neutral merely because it produces a number.
The target selected by the institution determines what the number means.
What AI May Do Better Than People
The use of AI in admissions is not automatically harmful. Some applications are genuinely promising.
AI Can Reduce Repetitive Administrative Work
Transferring transcript information by hand is slow and vulnerable to fatigue. A well-tested system can extract routine information quickly, allowing staff members to spend more time resolving unusual records and communicating with applicants.
Common App’s announced transcript-processing pilot was explicitly framed around reducing friction and supporting admissions teams. (Common App and EdVisorly announcement) (Edvisorly)
AI Can Apply a Defined Procedure Consistently
A machine does not become hungry at 4:17 p.m. It does not rush because twenty applications remain in the queue. It can apply the same programmed method repeatedly.
Virginia Tech cited greater consistency and speed as reasons for using its AI-assisted essay-scoring process. (Virginia Tech admissions FAQ) (Virginia Tech News)
Consistency is useful only when the procedure itself is valid.
A ruler that is consistently one inch short remains wrong.
AI Can Surface Errors or Outliers for Human Review
A system may identify:
A transcript value that does not match the application
An unusual grading pattern
A missing prerequisite
An essay score that differs sharply from a human score
A duplicated document
A possible data-entry error
A course sequence that deserves closer attention
Used carefully, AI can direct human attention rather than replace it.
AI Can Help Audit Human Decision-Making
Computational analysis can identify patterns humans may not notice:
Different ratings for similar applicants
Reviewer drift over time
Unequal disagreement rates across groups
Topics associated with demographic background
Schools whose records are frequently misclassified
Rubric categories applied inconsistently
Research on essay data has specifically proposed computational auditing as a way to examine possible bias in human and algorithmic readings. (arXiv)
An AI system can therefore be used to challenge human judgment, not merely imitate it.
That may be one of its most constructive roles.
What AI May Do Worse Than People
AI systems can process patterns at scale. They do not possess the full human context of an application unless that context is represented in the data and interpreted appropriately.
AI Can Mistake Proxies for Merit
A model may associate particular words, course patterns, activities, or writing styles with previous admissions outcomes.
Those signals may reflect:
School wealth
Access to counseling
Family educational background
Paid enrichment
Geographic location
English-language background
Familiarity with admissions conventions
The writing style of professional editors
Unequal opportunity to pursue certain activities
The model may label the pattern as predictive without understanding why it exists.
AI Can Flatten Unusual Applicants
An unusual student may be difficult to summarize.
Perhaps the activities do not form an obvious “spike.” Perhaps the student’s school uses narrative evaluations. Perhaps family work prevented conventional extracurricular participation. Perhaps the applicant’s intellectual strength appears through odd, self-directed projects rather than recognized competitions.
Human readers can pause.
They can reconsider.
They can say, “This file is strange, but there is something here.”
A system optimized for pattern recognition may be least comfortable with the applicant who does not resemble prior categories.
AI Can Overvalue What Is Easy to Measure
Course counts are easier to measure than intellectual courage.
Grammar is easier to measure than insight.
Leadership words are easier to measure than the quality of actual leadership.
A model may not deliberately ignore nuance. The institution may simply provide more structured data for easily quantified characteristics.
Over time, the measurable can quietly become the important.
That is a governance problem, not merely a technical one.
AI Can Reproduce Inconsistent Human Labels
A supervised model often learns from examples labeled by people.
If the human scores are inconsistent, the model learns inconsistency.
If the rubric rewards polished but conventional writing, the model may learn that preference. If readers historically misunderstood certain international curricula, a model trained on their decisions may encode those misunderstandings.
Automation can scale a good procedure.
It can also scale a weak one.
Much faster, naturally. The computer is very efficient that way.
Why Transparency Matters
Applicants are usually told what materials they must submit. They are less often told exactly how software transforms those materials after submission.
A meaningful AI disclosure should explain:
Whether AI is used
What application components it analyzes
What the system is designed to produce
Whether it summarizes, classifies, scores, or predicts
Whether a human reads the original material
How much influence the output has
What happens when the human and AI disagree
How accuracy is tested
How bias is evaluated
Whether applicants can correct processing errors
Which outside vendors receive applicant data
How long data are retained
Whether application data are used to train future models
The National Institute of Standards and Technology’s AI Risk Management Framework identifies validity, reliability, accountability, transparency, explainability, privacy, and fairness with harmful bias managed as central characteristics of trustworthy AI. NIST emphasizes that these qualities must be considered across system design, deployment, use, testing, and evaluation. (NIST)
An admissions office should not need to reveal source code or security-sensitive details to explain the system’s role.
It should be able to tell applicants whether a machine scores their essays.
The Civil-Rights and Fairness Questions
AI does not operate outside existing civil-rights obligations.
The U.S. Department of Education’s Office for Civil Rights has stated that federal nondiscrimination protections can apply when educational institutions use AI. A prior OCR guidance document explained that AI’s ability to operate at scale can create or compound discrimination involving race, national origin, sex, or disability. That document was later formally rescinded and remains online for historical purposes, so it should not be treated as current binding guidance; however, the underlying federal civil-rights statutes continue to apply independently of the withdrawn document. (Archived OCR resource)
Potential fairness questions include:
Does the model perform differently for multilingual applicants?
Does it misinterpret disability-related educational patterns?
Does it penalize unconventional schools?
Does it treat dialect differences as writing weakness?
Does it confuse access with ability?
Does it infer protected characteristics through proxy variables?
Does the system disadvantage students whose counselors provide shorter documents?
Does it work equally well across transcript formats?
Are rural, homeschool, international, tribal, military-connected, and nontraditional applicants represented in testing data?
Are applicants with interrupted education handled appropriately?
A model can have a high overall accuracy rate while producing much worse results for a smaller population.
“Accurate on average” is not enough for a high-stakes system.
The Privacy Problem Applicants May Not Expect
A college application contains unusually concentrated personal information.
It may include:
Family circumstances
Immigration information
Disability or health context
Financial details
Religious identity
School discipline
Trauma
Sexual identity
Political activity
Recommendation letters
Home address
Parent employment
Research records
Personal writing
Educational history
Applicants may assume that FERPA gives them broad rights to inspect how a prospective college uses those records.
That assumption has limits.
The U.S. Department of Education has explained that FERPA generally does not give an unadmitted applicant the right to access application materials maintained by a prospective college because the person is not yet a student in attendance there. State law and institutional policy may provide other rights, but rejected applicants often cannot use FERPA to demand the college’s internal admissions notes or AI-generated scores. (Department of Education letter regarding college applicants) (Student Privacy)
This makes transparency before submission especially important.
An applicant may never know that a model summarized, scored, or categorized part of the file.
Should Students Try to Optimize Their Applications for AI?
No—not in the way people usually mean.
Students should optimize for clarity, accuracy, context, evidence, and authentic human communication.
They should not:
Repeat presumed keywords
Write like a corporate leadership brochure
Add hidden text
Stuff activity descriptions with traits
Use strange formatting to confuse a model
Simplify sophisticated ideas unnecessarily
Add grammar mistakes to appear human
Imitate the style of previously admitted essays
Ask a chatbot to predict an undisclosed scoring system
Assume that every college uses the same software
Gaming an unknown algorithm is not a strategy. It is a superstition with bullet points.
How to Build an Application That Works for Human and Automated Review
Applicants cannot control an institution’s software, but they can reduce avoidable ambiguity.
Report Academic Information Exactly
Use official course titles, grades, dates, and institutions.
Do not rename “Introduction to Programming” as “Advanced Software Engineering.” Do not omit repeated courses, outside colleges, withdrawals, or educational transitions when the application requires them.
Automated transcript processing makes consistency especially important. A mismatch may be harmless, but it can create a flag requiring additional review.
Explain Unusual Academic Context Clearly
Use the Additional Information section or counselor materials to explain:
A school with no advanced courses
A nonstandard grading system
Block scheduling
International curriculum changes
Homeschool coursework
Dual enrollment
A course cancellation
A transfer between schools
Medical or family disruption
A transcript abbreviation that could be misunderstood
Do not assume a human—or machine—will know that “MST II” is the most advanced science course your school offers.
Keep Essays Specific
Specificity helps human readers understand the applicant and makes the essay less dependent on generic virtue language.
Weak:
Through leadership, I learned that serving others requires perseverance, teamwork, and empathy.
Stronger:
For three meetings, no one came. On the fourth Thursday, Elena brought her younger brother and asked whether he could use one of our calculators.
The second version does not announce the student’s character. It gives the reader evidence from which character may be inferred.
That remains valuable whether the essay is read by a person, summarized by software, or scored against a rubric.
Put Essential Meaning in the Actual Response
Do not bury the central point in an uploaded résumé, external link, portfolio description, or obscure formatting element unless the college specifically asks for it there.
A system may process only designated application fields. A human reader working quickly may do the same.
The application should remain understandable using the materials the college explicitly requests.
Make Activity Descriptions Verifiable
Describe:
What you did
Who benefited
How often you did it
What changed
What responsibility you held
What you created
What scale is accurate
What your individual contribution was
Avoid inflated claims such as:
“Transformed education globally”
“Revolutionized youth mental health”
“Led thousands” when the student managed an email list
“Published groundbreaking research” when the student performed limited data entry
AI-driven authenticity checks are not the only reason to be precise.
Experienced human readers have seen quite a few teenagers revolutionize the world before breakfast.
Make Research Understandable
A student submitting research should be able to explain it at several levels:
One sentence for a general reader
One paragraph describing the question and method
A technical explanation for a subject expert
A clear account of the student’s individual contribution
An honest description of limitations and failures
Save drafts, code history, laboratory notes, correspondence, mentor feedback, and contribution records.
The goal is not to create a defensive evidence warehouse. It is to preserve the natural record of real work.
Encourage Specific Recommendations
Recommendation writers should use examples rather than merely stacking adjectives.
“Brilliant, compassionate, hardworking, resilient, curious, and collaborative” contains six positive traits and almost no information.
A concrete classroom moment helps both human readers and any summarization system preserve meaningful evidence.
Students should not write their own recommendations unless the institution or recommender’s process explicitly permits student input in a transparent way. They can, however, provide recommenders with a factual résumé, project list, reflection sheet, or reminder of meaningful classroom experiences.
Preserve Consistency Across the Application
Consistency does not mean repetition.
It means the application’s claims do not quietly contradict one another.
Check whether:
The intended major fits the coursework and interests described
Activity hours are mathematically possible
Essay events match activity dates
Research claims match the mentor’s account
Leadership titles match actual responsibilities
Additional Information matches the transcript
Short answers sound like the same student
Awards are described accurately
The résumé does not introduce unreported institutions or experiences
Automated systems can make cross-document comparison easier. Humans also notice contradictions.
The best solution is not clever alignment.
It is accuracy.
Guidance for Multilingual and International Applicants
Do not flatten your language to sound like a stereotypical American teenager.
Formal writing is not evidence of AI. Neither is unusual syntax, advanced vocabulary, or the influence of another language.
At the same time:
Use language you understand
Do not accept automated rewrites you cannot explain
Preserve translated drafts
Follow each college’s rules about translation assistance
Explain unfamiliar school systems through official context
Confirm that course names and grades are reported correctly
Ask the counselor to clarify grading scales and curricular rigor
The greatest risk is not sounding “too international.”
It is losing your real meaning through excessive editing or inaccurate translation.
Guidance for Students With Disabilities or Interrupted Education
An automated rigor calculation may not understand why a student took fewer courses, changed schools, studied part-time, completed online coursework, or followed a modified schedule.
Provide necessary context without disclosing more medical detail than you are comfortable sharing.
Useful information may include:
The period affected
The academic consequence
The accommodation or schedule change
The resolution
Current evidence of readiness
For example:
A medical condition required a reduced course load during the spring of tenth grade. After treatment, I returned to a full schedule and completed advanced coursework in eleventh and twelfth grades.
That tells the admissions office what happened academically.
The application does not require a complete medical chart.
Guidance for Homeschool and Nontraditional Applicants
AI systems trained mainly on conventional transcripts may have difficulty with:
Narrative evaluations
Parent-created course titles
Competency-based credits
Mixed online and community-college curricula
Independent projects
Courses with no standard grade
Religious or classical curricula
International homeschool providers
Make the academic record legible.
Include, when permitted or required:
Course descriptions
Textbooks and major materials
Assessment methods
Instructor identity
Credit definitions
External grades
Community-college transcripts
Standardized assessments
Independent recommendations
The student should not redesign an authentic education merely to resemble the model’s presumed training data.
The institution should interpret the education in context.
The applicant’s job is to provide enough structure for that interpretation to occur.
What Applicants Can Reasonably Ask Colleges
Students do not need to send every admissions office an eighteen-question interrogation about its algorithmic governance program.
A few reasonable questions may be appropriate when the information is not publicly available:
Does the admissions office use AI to summarize or score essays?
Does a human read every required essay?
Is AI used to extract or evaluate transcript information?
Can applicants correct transcript-processing errors?
Is application information shared with outside AI vendors?
Is applicant data used to train models?
Is there a published policy describing AI in admissions review?
What happens when an automated result conflicts with human judgment?
The best place to begin is the college’s official admissions website, privacy notice, application terms, and technology or data-use policies.
Students should not assume silence means no AI is used.
They should also not assume silence means a hidden robot is deciding everything.
Silence means the information has not been publicly clarified.
What Responsible Colleges Should Do
Institutions considering AI in admissions should adopt procedures appropriate to the stakes.
Define the Use Narrowly
“Help with admissions” is too broad.
The institution should state whether the model:
Extracts data
Summarizes documents
Scores writing
Predicts outcomes
Detects anomalies
Routes applications
Recommends decisions
Verifies work
Generates communications
Validate the Actual System
A general claim that a technology is accurate is not enough.
The institution should test:
The specific model
The specific data
The specific applicant population
The specific task
The specific decision process
Audit Subgroup Performance
Overall accuracy can hide unequal error rates.
Audit results should examine meaningful populations, including those defined by:
Race and ethnicity
National origin
Language background
Sex
Disability
School type
Geography
Income context
Curriculum
Applicant type
Preserve Meaningful Human Authority
A human reviewer should be able to:
Access the original material
Understand the model’s function
Question the output
Correct errors
Override the recommendation
Document disagreement
Escalate unusual cases
Create an Error-Correction Process
Applicants should have a way to correct objective processing errors, such as:
Misread grades
Incorrect course classifications
Duplicate records
Missing documents
Wrong institutions
Incorrect residency fields
Misidentified curriculum
This is not the same as appealing a holistic admissions judgment.
It is correcting factual data.
Protect Applicant Information
Institutions should establish clear rules governing:
Vendor access
Data retention
Model training
Secondary use
Security
De-identification
Deletion
Cross-border processing
Staff access
Incident response
An application written for one admissions cycle should not quietly become permanent training material for an unrelated commercial product without clear authority and disclosure.
Frequently Asked Questions About AI Reading College Applications
Does Every College Use AI to Review Applications?
No public evidence establishes that every college uses AI in application review. Some institutions have disclosed specific uses, while vendors offer capabilities that colleges may choose to enable. Practices vary, and many institutions provide little public detail.
Can AI Reject an Applicant Automatically?
Publicly disclosed institutions often emphasize that humans remain responsible for admissions decisions. Virginia Tech says its AI system confirms human essay scores rather than making decisions. That does not prove automated rejection never occurs anywhere, particularly in processes involving minimum requirements or incomplete applications, but applicants should avoid universal claims without institution-specific evidence. (Virginia Tech News)
Are Colleges Using AI Detectors on Essays?
Some colleges may use software to evaluate authenticity, but public disclosure is inconsistent. An AI detector, an essay-scoring model, and an AI document summarizer are three different technologies.
Applicants should follow the application’s authorship rules rather than trying to satisfy an unreliable public detector.
Can AI Tell Whether an Essay Is Good?
A model can be trained to reproduce scores or identify features associated with a rubric. That is not the same as possessing a universal understanding of what makes an essay meaningful.
“Good” depends on the prompt, institution, context, evidence, voice, and purpose.
Should I Add Keywords Such as Leadership and Perseverance?
No. Demonstrate qualities through specific actions and reflection. Keyword stuffing can make an essay mechanical and may not reflect how any particular system works.
Will Perfect Grammar Improve an AI Score?
Possibly in a system that produces grammar-related data, but admissions essays are not grammar competitions. Clear writing matters; sterile perfection is not the goal. UNC says its AI provides data points about writing style and grammar, but its public FAQ does not explain a student-facing formula that applicants can or should optimize. (Undergraduate Admissions)
Can I Request My AI Score After Rejection?
Often not through FERPA alone. The Department of Education has stated that unadmitted applicants generally do not have FERPA access rights to application materials maintained by the prospective institution. State law or college policy may provide additional rights. (Student Privacy)
Will AI Hurt Creative Essays?
That depends on the system. A rigid model may be less comfortable with unconventional responses, while a human may appreciate them. Students should not eliminate creativity based on speculation. The safer strategy is controlled originality: make the essay unusual in substance or perspective while remaining clear enough to understand.
Can AI Understand My School Context?
It may process school and transcript data, but no applicant should assume that a model fully understands a school’s local curriculum. Use the counselor report, school profile, and Additional Information section to clarify genuinely unusual circumstances.
Is Human Review Automatically Fairer?
No. Humans have biases, inconsistent attention, fatigue, and subjective preferences. AI can sometimes improve consistency or help audit human patterns. The goal should not be “human good, machine bad.” It should be a transparent, validated process in which technology supports rather than obscures responsible judgment.
The Most Important Advice for Applicants
Do not write for the machine.
Write clearly enough that software does not distort the basic facts and specifically enough that a human remembers the person.
Report courses accurately.
Explain unusual context.
Make activity claims measurable and honest.
Understand your research.
Preserve drafts and contribution records.
Use your own voice.
Check the application for contradictions.
Do not turn yourself into a collection of admissions keywords.
An application is already a compressed version of a human life. AI may compress it again—into structured fields, summaries, features, ratings, or predictions.
Your task is not to prevent every form of compression. That is outside your control.
Your task is to make the original record accurate, coherent, specific, and difficult to misunderstand.
Key Takeaways for Students and Families
Some colleges publicly use AI to process transcripts, analyze essays, produce review data, or confirm human scores.
Virginia Tech says AI confirms human short-answer scores and does not make admissions decisions.
UNC says AI produces data points about essay writing, grammar, transcript rigor, and coursework.
Admissions software now offers AI summaries of essays and recommendation letters, but availability does not prove universal adoption.
Academic researchers have tested models that identify personal qualities, predict past decisions, and organize applicant pools.
Models can infer demographic or socioeconomic information from text even when those characteristics are not direct inputs.
AI may improve speed and consistency but can also reproduce historical bias, misread unconventional records, and flatten nuance.
Applicants should not try to reverse-engineer unknown models through keyword stuffing or artificial writing.
The most effective preparation is accurate reporting, clear context, specific essays, verifiable activities, and consistency throughout the file.
Colleges should disclose what their systems do, test them across populations, preserve human authority, protect applicant data, and provide ways to correct objective processing errors.
Sources and Further Reading
Virginia Tech Admissions Changes: Frequently Asked Questions
UNC Undergraduate Admissions: Does the Office Use AI and Why?
Using Artificial Intelligence to Assess Personal Qualities in College Admissions
AI and Holistic Review: Informing Human Reading in College Admissions
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests
Department of Education Letter Regarding FERPA and College Applicants
