Skip to content
AI College Prep Academy

Pre-Law Track · Inaugural Summer 2028

Senior engineer-led · Practicing PhDs co-teach · Selective admissions

Build the contract-review AI agent the next generation of attorneys will run inside their firm before their first year ends.

Capstone agent · Build legal AI.
I built an AI agent that reviews contracts and flags non-standard clauses.

High school student in a wood-paneled conference room reviewing a contract on a laptop, working on a contract-review AI agent

What your child will build

The contract-review agent

An agentic system that ingests a contract, compares each clause to a firm's standard, redlines deviations with reasoning, and produces a marked-up draft a junior associate can hand to a partner.

Claude APIModel Context Protocol (MCP)pgvector (clause database)Next.jsVercel deploy

What it does

  • Parses a contract into structured clauses (parties, obligations, remedies, governing law)
  • Compares each clause against a firm-specific standard library via RAG
  • Redlines non-standard language and explains why it deviates
  • Cites internal precedent and statutory language inline
  • Generates a partner-facing summary memo at the top of the redline
  • Tracks an audit trail of every flagged change for the associate's review
Law fellow archetype (sample profile) — Practicing attorney · Northwestern Pritzker Law, AI College Prep Academy Pre-Law co-teaching faculty (sample profile)

Co-Teaching Faculty

Law fellow archetype (sample profile)

Practicing attorney · Northwestern Pritzker Law

Our pre-law track is co-taught by a working law fellow — recruited from Northwestern Pritzker School of Law or a leading regional litigation or transactional firm — alongside a senior software engineer. Your child learns from an attorney who has lived inside the contract-review workflow, and who can articulate what a partner actually wants from a junior associate's first redline. The mentor relationship continues past the program: pre-law students who perform well receive a personal letter of recommendation and direct introductions into the broader practitioner network.

Students leave understanding what a partner reads first in a redline — and what citations earn the trust of a busy attorney.

Curriculum Deep-Dive

Week 2 — Legal AI deep-dive

The pre-law cohort splits off from the shared Week 1 foundations and works directly with the law fellow on contract structure, citation systems, and the framing problems unique to legal AI.

  • Clause taxonomy and contract parsing — turning prose into structured data
  • Citation-grounded retrieval — surfacing internal precedent and statutory authority
  • Redline reasoning patterns — why a clause deviates and what the firm's position should be
  • Partner-facing summary memos and the etiquette of a junior-associate handoff
  • Hands-on architecture review with the law fellow: framing, hallucination control, audit trails
  • Capstone scoping: pick a real contract type (NDA, MSA, employment), write the spec, agree on an evaluation rubric

Week 1 foundations are shared across all 5 tracksWeek 3 builds + admissions coaching

Sample student work

What your child could build.

Sample capstone projects illustrating the scope of Pre-Law work. Inaugural cohort is Summer 2028 — these are not real-alumni outcomes.

Sample student work — Maya R. (rising 11th)

NDA redline agent for a small-firm partner

Built an agent that compares incoming NDAs to a firm's standard form, redlines deviations, cites the partner's prior negotiation positions, and produces a 3-line summary at the top of the redline.

Claude + pgvector + Next.js + Vercel

Sample student work — Daniel K. (rising 12th)

Employment agreement comparison agent

Built an agent that reads two competing employment offers and produces a side-by-side comparison memo flagging non-standard clauses, citing relevant state law, and recommending negotiation priorities.

Claude + LangGraph + Postgres + Streamlit

Why this track wins for admissions

A capstone an admissions reader can verify.

An admissions reader at a top pre-law university — Ivy League undergraduate, accelerated 3+3 J.D., or BA-with-pre-law-track program — is looking for the rare applicant who can argue a position with structure, cite authority for it, and engage with the actual mechanics of legal practice. Most strong pre-law applicants present debate trophies, mock-trial wins, and student-government leadership — all important, all common. Far fewer can sit across from an interviewer and discuss the design tradeoffs in contract-review automation, the role of citation grounding in trustworthy legal AI, or how a junior associate actually hands work to a partner. Your child finishes the program with a working contract-review agent at a personal URL, an architecture writeup an admissions reader can verify, and a Common App essay built around the project — a piece of writing that makes their interest in law specific, structured, and verifiably their own.

Talk through your child’s admissions strategy
College admissions counselor reviewing a college essay draft with a high school student in a warm advisor's office

Pre-Law FAQ

Track-specific questions from parents.

Pre-Law · Selective by design

Ready to give your child the agentic-legal ai. advantage?

Selective admissions. We expect a 40 to 60 percent admit rate. Rolling applications through Spring 2028.