Kelly and Julian are joined by Microsoft Cloud Advocate Gwyneth Peña-Sigüenza to explore why working with small language models (SLMs) may be one of the best ways to understand AI. Rather than relying on increasingly capable models that hide complexity, Gwyneth argues that constraints build stronger fundamentals. From prompt engineering and context management to deployment and security, SLMs force learners to think more carefully about how AI actually works.

The conversation extends beyond AI models into learning itself. Gwyneth shares her self-taught journey from growing up on a remote farm in Ecuador with limited internet access to becoming a Microsoft Cloud Advocate and creator of the Learn to Cloud platform. Along the way, the group discusses productive struggle, mentorship, cloud engineering, Python, security, and what educators should prioritize as AI becomes part of every student's learning experience.

The episode closes with a thoughtful discussion about AI dependency, judgment, and whether we would actually flip the switch and turn AI off if given the choice.

Show Notes

Wins of the Week

• Gwyneth celebrates the New York Knicks reaching the NBA Finals after more than 50 years.
• Julian shares that he has accepted a new role as a Fractional CTO.
• Kelly reflects on taking her first real vacation in over a year—and how stepping away from work sparked unexpected ideas.

Small Language Models

• Why SLMs are valuable teaching tools
• Learning prompt engineering through constraints
• Running models locally on everyday hardware
• When local AI makes sense for classrooms
• Understanding tokens, context windows, and model limitations
• Why bigger models can sometimes hide important lessons

Learning Through Constraints

• Learning to drive in an old manual pickup truck as a metaphor for learning AI fundamentals
• Why difficult learning experiences often create lasting understanding
• Building strong habits before relying on more capable tools
• Consistency versus constantly chasing the newest resource

Self-Taught Learning

• Growing up without reliable internet in rural Ecuador
• Downloading YouTube playlists to learn programming offline
• Developing discipline through limited access
• The value of repetition and focused practice
• Why mentorship accelerates learning

Python Journey

• Transitioning from cloud engineering to Python advocacy
• Learning Python beyond scripting
• Discovering what "Pythonic" really means
• Wrestling with list comprehensions and other advanced syntax
• Favorite learning resources:
  • Fluent Python
  • Effective Python

Learn to Cloud

• Building an open-source cloud engineering curriculum
• Hands-on labs and automated verification
• AI-assisted assessment
• Supporting self-taught learners around the world
• Creating accessible technical education

Cloud, AI, and Security

• Deploying AI applications to the cloud
• Containers, virtual machines, and serverless deployments
• Why operations and security deserve more classroom attention
• Introducing secure development practices early
• The importance of authentication, secrets management, and responsible deployment

Teaching in the AI Era

• Helping students understand how AI works instead of simply using it
• Why productive struggle still matters
• The changing role of educators
• Balancing AI assistance with independent thinking
• Preparing students for a future where AI is always available

Final Thoughts

• AI dependency versus capability
• Judgment as the skill that matters most
• Human connection in an AI-driven world
• Would we actually turn AI off?
• Finding balance between technological progress and intentional learning

Support Teaching Python

Topics Covered

• Engaging Middle School Students: Strategies for capturing and maintaining student interest.
• Creativity and Collaboration: How to foster a collaborative environment that inspires creativity.
• Core Computational Concepts: Real-world applications that make these concepts accessible and interesting.
• Data Science Projects: Practical advice on integrating data science into your curriculum.
• Generative AI Ethics: Discussing the ethical use of AI in education.
• Python as a Teaching Tool: Exploring the versatility of Python for various projects.

Key Takeaways

• Integrating Computational Thinking Skills: Tips on how to weave these skills into everyday classroom routines.
• Teamwork and Communication: The importance of teamwork and effective communication in coding projects.
• Engaging Lessons with Python: How Python can be used to create engaging and meaningful projects for students.

About Kelly Powers

Kelly Powers transitioned from the business world to education, bringing a fresh perspective on teaching computational thinking. She offers invaluable insights into making rigorous and joyful learning experiences for middle school students.

Whether you are an experienced teacher or new to the field, this episode is packed with actionable ideas and inspirational moments that will help you create a more engaging and effective learning environment.

Tune in for a lively conversation that celebrates the magic of middle school teaching and the endless possibilities of computer science education.

Listen to the episode: Teaching Python Podcast

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Special Guest: Kelly Powers.

Support Teaching Python

Links:

• CodeHS - Teach Coding and Computer Science at Your School | CodeHS — Everything You Need, All In One Spot CodeHS is trusted by thousands of teachers and schools all over the world.
• Overview ‹ Scratch — MIT Media Lab — Scratch is the world's most popular coding community for kids. Millions of kids around the world are using Scratch to program their own interactive stories, games, and animations—and share their creations in an active online community.
• Welcome To Colab - Colab — Colab is an online Jupyter notebook from Google
• Computer Science Teachers Association Connect, Grow, & Share With CS Teachers- — CSTA understands that teaching computer science is hard. That’s why we’re focused on creating a supportive environment for K–12 educators.