8 Insights from Stanford’s Youngest Instructor: AI, C++, and the Evolution of CS Education

In a recent podcast on freeCodeCamp, Quincy Larson sat down with Rachel Fernandez, a computer science student at Stanford and the university's youngest-ever instructor. Rachel's journey from a low-income town in California to teaching at a world-class institution is inspiring. Her discussion covered the state of CS education in 2026, the enduring relevance of C++, and how developers can leverage AI without losing their edge. Here are the top eight takeaways from her conversation—dive in to learn from her unique perspective.

1. From Westminster to Stanford: A Trailblazer’s Story

Rachel grew up in Westminster, a small California town with a predominantly Mexican and Vietnamese population. At her high school, 70% of students qualified for free lunches due to low family incomes. She became the first student from her school to attend Stanford in years—a testament to her determination. This background shapes her view on education: she believes that access and opportunity are critical, and she encourages underrepresented students to pursue tech careers.

2. TreeHacks: A Massive Hackathon with Million-Dollar Stakes

Rachel helped organize TreeHacks, Stanford’s annual hackathon. This year, the event drew 15,000 applicants, but only 1,000 participants were selected. Over a single weekend, teams built innovative projects and competed for $1 million in prizes. The competition was fierce, but the experience highlighted how hackathons can accelerate learning and creativity. Rachel notes that such events are a fantastic way to network and build portfolio pieces.

3. Why C++ Still Matters in 2026

As the instructor of Stanford’s C++ course, Rachel is a strong advocate for the language. Despite the rise of newer languages, C++ remains critical for performance-intensive applications like game engines, operating systems, and real-time systems. She emphasizes that learning C++ forces developers to understand memory management and low-level operations—skills that transfer to other languages. It’s not about ignoring modern tools, but about building a solid foundation.

4. The State of Computer Science Education in 2026

Rachel sees a growing gap between traditional CS curricula and industry needs. Many universities still focus heavily on theory, while practical skills like DevOps, cloud computing, and AI integration are often undervalued. She advocates for more hands-on project work and for educators to keep pace with technological shifts. “We need to teach students how to learn continuously,” she says, “because what we cover today may be obsolete tomorrow.”

5. Using AI Without Deskilling Yourself

AI coding assistants like GitHub Copilot are powerful, but Rachel warns against becoming too reliant. She recommends developers use AI as a co-pilot—not an autopilot. The key is to understand the generated code, question its outputs, and maintain your problem-solving abilities. She suggests using AI to handle boilerplate tasks while focusing your mental energy on architecture and design. That way, you stay sharp and grow as an engineer.

6. Automation: The Developer’s Secret Weapon

Rachel highlights the importance of automation for productivity. freeCodeCamp recently published a course on building automation workflows using triggers and actions. You can learn to create a Model Context Protocol (MCP) server to share data between apps and agents. Automating routine tasks frees up time for innovation. Rachel herself uses automation to streamline administrative work, allowing her to focus on teaching and coding.

7. Data Quality: Preventing Bad Data from the Start

Bad data can cripple a system. In another freeCodeCamp resource, Rachel points to a handbook that covers common data errors and validation layers: frontend, backend, database, business logic, and ingestion. She stresses that testing at each layer is essential. Developers must think proactively about data integrity. She recommends implementing checks early in the pipeline to avoid costly fixes later. This mindset is especially crucial in AI projects, where garbage in equals garbage out.

8. AI Governance: Not Just a Management Concern

Finally, Rachel dives into AI governance—often dismissed as a managerial issue. But she argues that developers are on the front lines of building responsible systems. freeCodeCamp’s handbook on AI governance includes hands-on Python projects: a model card generator, a bias detection pipeline, an audit trail logger, and a human-in-the-loop escalation system. These tools empower developers to embed fairness and transparency into their products.

Rachel’s journey and insights offer a roadmap for anyone navigating the evolving landscape of computer science. From mastering C++ to using AI wisely, her advice is practical and forward-looking. For the full interview, watch the podcast on the freeCodeCamp YouTube channel or listen on your favorite app. And don’t forget to check out the freeCodeCamp resources mentioned above—they’re packed with actionable knowledge.