7 Key Insights from Stanford's Youngest Instructor on AI, Education, and Tech Ethics

In a recent episode of the freeCodeCamp podcast, Quincy Larson sat down with Rachel An Fernandez, a computer science student at Stanford who holds the distinction of being the university's youngest instructor. Rachel's journey is remarkable: she grew up in Westminster, a small California town where 70% of her high school classmates qualified for free lunches, and became the first student from her school to attend Stanford in years. She recently organized TreeHacks, Stanford's annual hackathon, which drew 15,000 applicants for only 1,000 spots. In this article, we distill the key takeaways from their conversation—covering the evolving state of computer science education, the enduring relevance of C++, practical tips for using AI without losing your edge, and more. Each insight is paired with free resources from freeCodeCamp to help you dive deeper.

1. Who Is Rachel An Fernandez?

Rachel An Fernandez is not your typical Stanford student. At just 20 years old, she is simultaneously a computer science undergraduate and the youngest instructor at the university—a role that sees her teaching C++ to other students. Her path to Stanford was anything but privileged. Growing up in Westminster, a predominantly Mexican and Vietnamese community, she attended a high school where the majority of families were low-income. Rachel became the first person from her high school to be admitted to Stanford in years, a testament to her determination and talent. Her story underscores how diverse backgrounds can enrich the tech community, and she now mentors and inspires others who face similar barriers.

2. The State of Computer Science Education in 2026

Rachel offered a candid assessment of computer science education as of 2026. While universities like Stanford continue to produce excellent engineers, she noted that the rapid pace of technological change often leaves curricula trailing behind. Traditional CS programs still emphasize fundamentals like algorithms and data structures, but emerging fields such as AI, cybersecurity (InfoSec), and ethical hacking are not always integrated quickly enough. Rachel believes that hands-on projects—like hackathons—are essential for bridging the gap between theory and practice. She advocates for more interdisciplinary learning, where students combine CS with domain knowledge. The key takeaway? Formal education is a foundation, but continuous self-learning and community involvement remain critical.

3. Why C++ Still Matters (According to a Stanford Instructor)

One of Rachel's specialties is teaching C++, a language often considered dated by students eager to jump into Python or JavaScript. She argues that C++ remains vital for systems programming, game development, and performance-critical applications. Learning C++ forces you to understand memory management, pointers, and low-level operations—skills that make you a stronger developer in any language. Rachel also points out that many modern AI frameworks and operating systems are built on C++ foundations. Her advice: don't skip the hard stuff. Mastering C++ can give you a deeper appreciation for how computers work, and it's still widely used in industries like finance, robotics, and embedded systems.

4. How to Use AI Tools Without Deskilling Yourself

Rachel addressed a growing concern among developers: relying too heavily on AI coding assistants like GitHub Copilot or ChatGPT can erode problem-solving abilities. She emphasizes the importance of understanding the code that AI generates, rather than blindly accepting it. Her tip: treat AI as a pair programmer or a brainstorming partner, not a crutch. Before using an AI tool, try to solve the problem manually or outline a solution. Then use the AI to fill in repetitive parts or suggest alternatives. To help developers stay sharp, freeCodeCamp recently published an AI Assisted Coding handbook that covers best practices for integrating AI into your workflow without sacrificing skills.

5. TreeHacks: Inside Stanford's Premier Hackathon

Rachel served as a key organizer for TreeHacks, Stanford's annual hackathon that attracted 15,000 applicants this year. Only 1,000 participants were selected to build projects over a single weekend, competing for a million dollars in prizes. The event is a microcosm of the tech industry's fast-paced, collaborative spirit. Rachel shared that the most successful teams combined diverse skill sets—front-end, back-end, design, and domain expertise—rather than focusing solely on coding. She also noted that many projects leveraged AI and machine learning, reflecting current industry trends. TreeHacks demonstrates how intense, time-bound challenges can spark innovation and create lasting connections among developers.

6. Overcoming Adversity: From Westminster to Stanford

Rachel's personal story is one of resilience and access. Growing up in a low-income community, she had limited exposure to technology careers. She credits teachers and mentors who introduced her to coding, as well as her own grit. Her journey highlights systemic issues in education: students from underprivileged backgrounds often lack the resources to compete. Rachel is now passionate about outreach and mentorship, speaking at schools and events to encourage others. She believes that the tech industry must actively work to level the playing field, whether through scholarships, free educational content, or inclusive hiring practices. Her example proves that talent exists everywhere; opportunity shouldn't be a barrier.

7. Building Responsible AI Systems (It's a Developer's Job)

AI governance might sound like a management concern, but Rachel insists it's developers who implement responsible systems daily. From bias detection to audit trails, engineers need to embed ethics into their code. freeCodeCamp has released a comprehensive AI Governance handbook with four hands-on Python projects: a model card generator, a bias detection pipeline, an audit trail logger, and a human-in-the-loop escalation system. Rachel recommends developers familiarize themselves with these tools to stay ahead of regulations and build trustworthy applications. She also notes that understanding AI's limitations—like hallucination and data biases—is crucial for anyone leveraging large language models in production.

We hope these insights from Rachel An Fernandez inspire you to explore computer science with renewed curiosity. Whether you're learning C++, participating in hackathons, or integrating AI into your toolkit, remember to never stop questioning and always keep your skills sharp. For more resources, check out freeCodeCamp's latest courses on automation and data quality—and as Rachel shows, the journey is just as important as the destination.