Skip to main content

AI Studio Teams: Portfolio-Based Pathways to AI-Era Careers

Stanford CREATE AI Challenge | Track 3: Augment Career Opportunities

Funding Request: $50,000 Implementation Partner: El Segundo Unified School District Project Duration: 12 months


Executive Summary

The pathway from education to employment is breaking. As AI transforms the workforce, entry-level positions are disappearing at an alarming rate—creating an "experience paradox" where young people cannot gain experience because employers no longer offer the entry points that traditionally provided it. This isn't a future problem—this is the landscape TODAY's students will enter within 24-48 months.

AI Studio Teams addresses this crisis by rebuilding the bridge to careers—a structured approach to nurturing human potential through verified capability. Our model replaces the broken credential-to-career pipeline with a portfolio-based pathway where cross-grade teams of 8-12 high school students complete authentic projects for local employers, building verified portfolios that prove their ability to work effectively alongside AI systems.

Don't Give Them the Fish. Teach Them to Fish.

Most AI education gives students prompts that expire in months. We teach metacognitive skills through real business challenges—skills that transfer across any AI tool, because tools change, but the way you THINK persists. Through the SkaFld Ideation Methodology, students learn not just how to use AI tools, but how to THINK about problems—combining decomposition thinking, cross-domain pattern recognition, and responsible AI practices.

This program puts educators and learners at the heart of AI design—ensuring these tools expand access, agency, and connection, and in turn advance learning, well-being, and opportunity. Like Stanford's commitment to interdisciplinary learning, we combine technical capability with policy awareness and strategic thinking for responsible AI deployment.

The Innovation

Unlike traditional career preparation that relies on classroom simulation, AI Studio Teams operates at the intersection of four transformative approaches:

  1. Portfolio Over Credentials: Students graduate with employer-validated work samples, not just transcripts
  2. Near-Peer Mentorship: 12th graders mentor 10th graders; 11th graders mentor 9th graders, creating sustainable learning communities
  3. Employer Integration: Local companies provide real projects, quarterly portfolio reviews, and micro-internship pathways
  4. Responsible AI Foundation: Students learn ethical AI interaction—including hallucination detection and appropriate use boundaries—preparing them to work alongside AI systems with discernment

Why El Segundo

El Segundo represents an ideal implementation environment: a compact district (5 schools, 3,400 students) with direct access to major employers including aerospace leaders (Boeing, Northrop Grumman), technology companies, and entertainment industry headquarters. This concentration enables authentic employer partnerships and interdisciplinary practice that would be impossible in larger, more dispersed districts.

Expected Outcomes (12 months)

MetricTarget
Students with employer-validated portfolios96 students (8 teams of 12)
Employer partners providing real projects12 companies
Micro-internship placements24 students (25%)
Portfolio quality score (employer-rated)7.0/10 minimum
Gender parity in participation50/50 target
Stanford Alignment

This proposal directly addresses CREATE AI's Track 3 focus on "AI solutions that support skill-building, mentorship, and pathways to meaningful work." AI Studio Teams transforms how students develop career-ready capabilities by embedding AI collaboration into authentic project work validated by actual employers.


Proposal Contents:

  1. Problem Statement - The Entry-Level Job Crisis
  2. Solution - AI Studio Teams Model
  3. Learning Science - Evidence Base
  4. Outcomes - Measurement Plan
  5. Equity - Fairness & Inclusion
  6. Timeline - Implementation Roadmap
  7. Team - Project Leadership
  8. Budget - Resource Allocation