Program Overview
Silicon Valley remains the epicenter of educational technology innovation. This module explores how AI and machine learning are transforming personalized learning, adaptive assessment, and educational data analytics.
Students will engage with EdTech startups, venture capital firms, and research institutions to understand the intersection of learning science, product development, and business models in the education sector.
Key Learning Outcomes
- Analyze how adaptive learning algorithms personalize educational content
- Evaluate EdTech business models and their scalability
- Understand the role of data analytics in measuring learning outcomes
- Explore ethical considerations in educational technology
- Develop frameworks for assessing EdTech product-market fit
Industry Partners
- Stanford d.school: Design thinking workshops for educational innovation
- EdTech Accelerators: Site visits to Y Combinator and LearnLaunch
- Venture Capital Firms: Sessions with education-focused VCs
- Learning Analytics Labs: Research presentations from Stanford GSE
Daily Itinerary Sample
Day 1-2: Foundation
- Introduction to learning science and educational technology
- Workshop on adaptive learning algorithms
- Case studies of successful EdTech companies
Day 3-4: Industry Immersion
- Site visits to EdTech startups
- Meetings with product managers and engineers
- Venture capital firm presentations
Day 5-6: Research & Analysis
- Stanford GSE research presentations
- Data analytics workshops
- Student project development
Day 7-8: Synthesis
- Final project presentations
- Industry feedback sessions
- Networking events
Assessment
Students complete a research project analyzing an EdTech company’s approach to personalized learning, including technical analysis, business model evaluation, and ethical considerations.
Prerequisites
Background in computer science, education, or business preferred. Familiarity with basic machine learning concepts helpful but not required.
