Teaching Python

How do you keep learning when there never seems to be enough time? The answer may be less about finding more time and more about building learning into the way you already work, solve problems, use AI, and connect with other people. In Episode 163 of the Teaching Python Podcast, Sean Tibor, Kelly Schuster-Paredes, and Julian Sequeira discuss how they keep learning in a technology landscape shaped by rapid changes in AI, software development, and computing. They compare practical approaches to continuous learning, including building projects, using commutes and downtime, scheduling dedicated learning time, asking ChatGPT and Claude questions as they come up, watching webinars, reading, experimenting with new tools, and learning from professional networks. The conversation also explores what happens when you need to learn something you are not naturally interested in. They discuss adult learning, desirable difficulty, just-in-time learning, recursive learning, and why struggling with unfamiliar ideas can still be valuable even when AI can explain concepts instantly. Along the way, they talk about vibe coding, AI as a learning partner, professional development, technical communities, balancing creation and consumption, and setting boundaries so that continuous learning does not become constant burnout. If you are trying to keep up with AI, learn new technology, or continue developing technical skills without turning every spare moment into work, this episode offers a practical look at how learning can become part of everyday life.

What does teaching Python mean in 2026, when AI can generate code and programming sits inside a much larger computing landscape? Teaching Python started as a podcast about teaching programming in the classroom, but both the hosts and the world around Python have changed. In this episode, Kelly and Julian reflect on how Python education now connects with AI, data, cybersecurity, cloud computing, software engineering, systems thinking, and automation. They discuss how their own roles have evolved from teacher, coder, and technologist into work that crosses disciplines, and why understanding technology now requires more than simply learning how to write code. The conversation explores why learning Python still matters in the age of AI-generated code, why reading, debugging, evaluating, testing, and modifying code may be more important than ever, and how teachers and learners can build the judgment needed to work with increasingly capable AI tools. Python is still here. But teaching Python in 2026 is increasingly about how people learn, build, reason, and make decisions with technology.

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. 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