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.

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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.

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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.

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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.

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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.

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In this episode, Python Developer Advocate and author Will Vincent joins the hosts to discuss the lasting appeal of Django, changes in how people learn web development, and the ways AI is reshaping software engineering. While modern AI tools can generate working code in seconds, Django's opinionated design and emphasis on maintainability help developers avoid many of the security and architectural problems that often emerge as projects grow.

Drawing on his background as an educator, author, and Developer Advocate at JetBrains, Will shares his perspective on the challenges facing today's developers and computer science students. The conversation touches on "vibe coding," the misconception that a successful prototype automatically translates into a production-ready application, and the increasing burden AI-generated content is placing on open-source maintainers. Will also discusses the rise of specialized AI models, the importance of human trust in technical communities, and why foundational software engineering skills remain valuable despite rapid advances in AI tooling.

Key Topics Covered

Why Django Still Matters

The Reality Behind "Vibe Coding"

Learning to Program as an Adult

AI and Programming Education

The Growing Burden on Open Source

Local and Specialized AI Models

Developer Concerns in the AI Era

Resources Mentioned

Special Guest: Will Vincent.

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Kelly talks with Philip Guo, creator of Python Tutor, about how the tool helps students trace code and understand programming basics. They also discuss the challenges AI-generated code creates in the classroom and possible ways to support student learning.

*Wins of the Week

*AI, Coding, and Classroom Understanding

Python Tutor and Possible AI Features

Philip explains that Python Tutor currently visualizes execution and has an AI chat feature that can answer questions about code and errors. They discuss possible future features, including simplified AI-generated examples, alternative execution views that show only the lines actually run, and more guided inline help tied to specific code or variables.

Oral Explanations and Assessment

Kelly describes using a Socratic-style code review with students, where they discuss code aloud in groups. They also talk about using spoken explanations or short oral assessments to check whether students can really explain what code is doing, rather than just copying or prompting AI-generated answers.

Broader Research and “Beyond the Desk”

Philip briefly discusses a new research direction with a PhD student focused on AI support for work beyond the desk, including physical and embodied tasks in science labs and fieldwork. He says this differs from desk-based AI work and involves activities that are harder for current AI systems to support.

**Chapters

Special Guest: Philip Guo.

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In this episode of Teaching Python, Kelly Schuster-Paredes and Julian Sequeira are joined by engineer and maker Todd Kurt to discuss what happens when code leaves the screen and starts interacting with the physical world. The conversation centers on CircuitPython, MicroPython, and physical computing, with a focus on how these tools are used in classrooms and maker projects.

Todd explains his background in engineering, web development, and open source hardware, including his work on LED devices and his recent focus on CircuitPython. He describes the differences between CircuitPython and MicroPython, emphasizing that CircuitPython is designed to feel closer to desktop Python and to support teaching, while MicroPython makes more efficiency-focused tradeoffs.

The discussion also covers the practical challenges of hardware-based learning. Todd and the hosts talk about bootloaders, UF2 files, board compatibility, library management, and common mistakes such as using the wrong cable, the wrong board file, or wiring power and ground incorrectly. They note that these issues can make hardware feel frustrating, especially for beginners and teachers preparing classroom kits.

Kelly and Julian share their classroom experiences, including using preloaded boards, NeoPixels, sensors, and simple student-designed projects. They discuss how hardware can support troubleshooting skills, file-system awareness, and persistence, and why students often engage more when they are building something tangible, such as a sensor-based wearable or a small robot.

The episode also includes Todd’s stories about early embedded work, including a costly lab mistake, and his involvement in hardware that contributed to space missions. He closes by describing a compact synthesizer project built around a Raspberry Pi Pico and by noting that he shares work through his website and online accounts.

Special Guest: Tod Kurt.

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In this episode, Sean, Kelly, and Julian tackle a provocative question: is the traditional "Hello, World" first program dead? What was once a thrilling moment of agency — telling a computer to do something and watching it respond — now competes with AI assistants, voice interfaces, and tools that can build entire applications from a single prompt.

The conversation dives into the different types of learners Kelly encounters in her classroom: the students who want AI to do everything, the ones who light up when they catch AI writing unused functions, and the old-school coders who just want to write it themselves. Sean shares how he turned a massive org design challenge at work into a Python project with a SQLite database, proving that the best way to learn is still to find a real problem and solve it with code.

Kelly describes her fourth-quarter experiment to create a new "Hello, World" moment for her 8th graders using school-approved AI tools, while Julian raises the important question of whether the real challenge is just showing people that code can solve their problems in the first place. The trio also explores whether AI can strip away the administrative clutter in teaching to let educators focus on what matters: engagement, personalization, and good pedagogy.

The episode wraps with two pieces of news: the PyCon US Education Summit is confirmed for Thursday, May 14th, and Julian Sequeira is officially joining the show as a regular co-host — complete with a live, slightly fumbled first sign-off.

Kelly: Bringing two Pine Crest colleagues to PyCon US this year — Chris and Kayla, an aspiring data scientist who is excited to dive into Python and attend the Education Summit.

Julian: His 10-year-old son scored his first basketball basket after multiple seasons of showing up, practicing, and persisting — a nothing-but-net shot that had the entire gym erupting.

Sean: Used Claude to create a comprehensive, interactive study guide from his daughter's 11-page science PDF on water quality — complete with clickable concept maps, pH level visualizations, and chain-of-events diagrams that made 7th-grade science genuinely engaging.

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What does it really mean to be "techie"? Sean, Kelly, and guest Amelia Hough-Ross dig into the labels we put on ourselves and others — and why curiosity and persistence matter more than credentials. From imposter syndrome to productive struggle, this episode redefines what it means to be technical in a rapidly changing world.

"It's hard to think outside of the box when you don't know what's inside of the box." — Kelly, quoting a conference in Tampa

"The difference between viewing yourself as technical and not technical is getting those successes... even just once, where something really cool happens that you weren't expecting to work." — Sean

"It's much harder to believe that someone has that greatness in them and help them achieve it... It's easy to say someone's hopeless. The harder part is figuring out how to support them to get to that next level." — Amelia

Special Guest: Amelia Hough-Ross.

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