Technology and equity in college access, read through the emerging artificial intelligence literature of 2017 to 2026.
Artificial intelligence now touches nearly every stage of the road to college, from the summer melt that strands admitted students to the essays that decide who is admitted. Whether it widens that road or narrows it does not turn on the technology itself. It turns on three questions.
Predict as you go. Watch one principle act on your own words. The score is not the point, the noticing is.
The first wave of AI in college access was built and run by institutions. Universities deployed conversational chatbots against summer melt, the gap between admission and matriculation that swallows an estimated 10 to 20 percent of college-intending graduates each year, with the heaviest losses among low-income and first-generation students.
The mechanism is structural. Because melt concentrates among the least-resourced, a tool that simply reaches whoever is stuck on a required task reaches the marginalized first. (Page & Gehlbach, 2017)
Georgia State gave every admitted student an automated texting assistant to guide them through their summer to-do list, from financial aid to registration.
That chatbot cut summer melt at Georgia State. Researchers then ran the exact same tool at East Carolina University, a campus serving more advantaged students.
Admissions officers fear AI essay scoring would bake in bias, since test scores already track family income and race. Researchers trained a model to rate seven personal qualities, then audited it across demographic subgroups.
Fairness here was measured in a low-stakes research setting. Campbell's law warns that the more weight a measure carries in a real decision, the greater the incentive to game it, and better-resourced applicants are best positioned to do so. The model also misses what humans catch: it scored "I donated heroin to the children's shelter" as highly prosocial.
Generative AI changed who holds the tool. The student now brings ChatGPT to the application. Control shifts from the institution, which targeted help at concrete barriers, to the individual, whose use is unguided and whose model speaks in a borrowed voice.
The same digital divide that helped in Era I begins to work in reverse.
Researchers had GPT-3.5 and GPT-4 write 25,000 admissions essays, then compared their style against 150,000 real student essays whose authors' backgrounds were known.
Using Gramsci's idea of linguistic hegemony, the authors show the default voice tracks the dialect of privilege, the same writing already rewarded in holistic review. This is about whose style AI imitates, not admission outcomes, and the gender pattern holds only on the most gendered features.
Alvero showed AI's default voice sounds privileged. Do not take my word for it. Pick a sentence written in a vivid, personal voice, or type your own, and run it through the machine. Watch what "polishing" removes.
The distinctiveness holistic review is meant to reward gets blended away toward the voice of privilege. And the students who lean on this tool most are lower-income (Lee et al., 2026), so the instrument meant to set them apart instead makes them blend in.
A scripted illustration of the documented direction of the effect, not a live language model.
This is observational, not causal. The honest claim is association, not that AI caused the gap. Here is why that distinction matters.
One selective institution, one preprint. The pattern is a warning worth investigating, not a settled finding. Intellectual honesty is part of the lecture.
You deploy the AI. Choose who holds it, and watch the equity needle move.
It integrates with student records, targets whoever is behind on a required task, and reaches first-generation and low-income students first. The divide bends toward equity.
Use is unguided, lower-SES students lean on it more, and its default voice carries the linguistic capital of the already-advantaged. The divide bends back toward inequity.
The technology is not the variable. The locus of control is.
Prompted with African American English instead of Standardized American English, with race never named, language models judged the speaker more harshly. The bias was covert, triggered by dialect alone.
Before AI enters, the access gap is already stark. The least-resourced, most open-access institutions support disabled students least.
Across income, race, and disability, one pattern repeats: underrepresented groups inherit the bias, and the standard fixes hide the surface while the deeper bias persists.
Who builds it. Institution-built systems can be fair and can target need (Lira; Page & Gehlbach; Nurshatayeva et al.).
Who uses it. Uncontrolled student-side use bends the divide back toward the advantaged (Alvero; Lee et al.).
Whose voice trained it. Across income, race, and disability, underrepresented groups inherit the bias, and scaling does not erase it (Hofmann; Beck Wells; Dumitru et al.).
AI widens access when an institution owns it, points it at a concrete barrier, and audits it. It narrows access when it is left to unguided use inside the judgments meant to capture a student's individuality. And the higher the stakes, the stronger the pull to game the signal, where the better-resourced are always better positioned.
You predicted, you watched a voice flatten, you chose where to deploy. That active engagement, the prediction and the feedback, is the same mechanism that let institutional chatbots close gaps in Era I: support works best when it reaches you where you are.
The score was never the point. The point is who holds the tool, and toward whom it is pointed. Today, that was you.
Seven peer-reviewed studies and one preprint (Lee et al., 2026), which is not peer-reviewed and is cited as emerging evidence only.