In this episode of Teaching Python, Kelly and Julian welcome IBM Distinguished Engineer Jeff Crume to talk about teaching hard things simply.

The conversation begins with Jeff’s short, visually driven videos and the studio setup behind them, including the lightboard format, the editing process, and the amount of planning needed to turn a 15-minute explanation into something clear and usable.

They then turn to the challenge of explaining complex ideas in a way that fits the audience. Jeff describes how he chooses topics from his work with clients, why he thinks teaching deepens his own understanding, and how he adapts for YouTube, classrooms, and conference talks. He emphasizes brevity, structure, and using visuals so viewers are not faced with a talking head and a blank background.

A major part of the discussion focuses on AI, the humanities, and education. Jeff explains why he believes the humanities are essential for understanding meaning, purpose, truth, and context, and why those questions matter when using AI. He argues that AI should be treated as a tool to augment learning rather than something to exclude from classrooms.

The conversation also covers cybersecurity and practical AI risks. Jeff discusses passwords versus passkeys, phishing, public chatbots, data privacy, cloud services, and the security concerns around agents and connected tools. He argues for private instances, stronger security practices, and doing security earlier in the process.

Near the end, Jeff highlights communication, curiosity, and critical thinking as key skills for students. He also points listeners to IBM SkillsBuild and Coursera for training, and closes by encouraging lifelong learning in a fast-changing field.

Special Guest: Jeff Crume.

In this episode, Kelly Schuster-Paredes speaks with Mahmoud Harding about his work in data science education and the way he thinks about teaching Python, R, and statistics. Mahmoud explains that he is the instructional design director at Data Science for Everyone, where the goal is to make data science available to more students and to connect it to meaningful, real-world contexts.

A major part of the conversation focuses on how students learn best through curiosity and project-based work. Mahmoud describes the ADAPT model, including its emphasis on project-based learning and common learning elements, and he argues that students should begin working with their own data early in a course. Kelly and Mahmoud discuss how choosing their own datasets helps students become more engaged, notice mistakes, and ask better questions.

The discussion also compares R and Python as tools for data science. Mahmoud explains that R was designed by statisticians for statistical analysis, while Python became popular as a general-purpose language that later grew into a strong data science ecosystem through libraries like NumPy and pandas. He also describes Jupyter Everywhere, a browser-based notebook environment designed to reduce barriers for schools and allow students to use R or Python without complicated setup.

Later, the conversation turns to judgment, nuance, and the role of data in learning. Mahmoud argues that students need domain knowledge and human judgment to interpret data responsibly, and that data projects can help them develop those skills. Kelly extends this idea to other subjects, suggesting that books, history, and other classroom materials can also be treated as data for analysis and discussion.

The episode closes with Mahmoud sharing ways to connect with him through Data Science for Everyone and with mention of an upcoming Data Science Education K–12 event in Atlanta in February.

Special Guest: Mahmoud Harding.

What can small language models teach us that the largest AI models cannot?

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.

• 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.

• 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 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

• 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

• 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

• 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

• 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

• 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

• 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