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    Education

    Teaching Python

    Teaching Python is a podcast about Python programming, computer science education, AI literacy, software development, cloud computing, cybersecurity, data, and how people learn technical skills. Hosted by Kelly Schuster-Paredes, Sean Tibor, and Julian Sequeira, the show is for educators, developers, technology leaders, and lifelong learners who want to better understand how Python connects to the wider world of computing. Episodes explore not only how people learn to code, but also how they build technical judgment, understand systems, evaluate AI-generated code, work with data, think about security, and move from beginner programming into real-world software development. About the Hosts Kelly Schuster-Paredes is a teacher who codes whose work has expanded from classroom computer science into AI strategy, curriculum design, professional learning, educational technology, and responsible technology adoption. Her background in Python and computer science education shapes her focus on learning, AI literacy, computational thinking, and what people need to understand in an AI-shaped world. Sean Tibor is Vice President of Infrastructure and Cloud at Pfizer and a former computer science teacher. He brings expertise in cloud computing, infrastructure, engineering operations, and technical leadership, connecting what people learn about computing with how large-scale systems are actually built, operated, secured, and maintained. Julian Sequiera is a technologist, Fractional CTO, and Senior Program Manager with more than 20 years of experience in infrastructure, cloud, engineering operations, and large-scale technology programs. He is also the co-founder of PyBites, a Python learning platform and community that has helped thousands of developers improve their Python and software development skills. What We Cover Python Programming and Computer Science Education: Learning Python, teaching programming, computational thinking, debugging, code literacy, and helping beginners build strong mental models. AI and AI Literacy: AI-assisted programming, evaluating AI-generated code, responsible AI use, human judgment, and what learners still need to understand when AI can produce code. Cloud, Infrastructure, and Cybersecurity: Systems, networks, deployment, security, reliability, architecture, and the operational side of software. Data and Software Engineering: APIs, databases, testing, maintainability, version control, software design, and moving from simple scripts to real-world applications. Learning and Technical Growth: How people learn difficult technical concepts, get unstuck, build confidence, and develop the judgment needed to use technology well. Expert Interviews: Conversations with educators, developers, engineers, researchers, technology leaders, and others shaping the future of computing and technical education. Teaching Python remains grounded in Python, but the conversation extends beyond syntax. The podcast explores the knowledge, skills, systems, and judgment people need to learn, build, and make responsible decisions with technology.

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    Latest Episodes:
    Episode 163: How Do You Find Time to Keep Learning? Oct 02, 2026
    Show notes

    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.

    Support Teaching Python

    Episode 162: What Does “Teaching Python” Mean in 2026? Sep 19, 2026
    Show notes

    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.

    Support Teaching Python

    Episode 161: Teaching Hard Things Simply Aug 07, 2026
    Show notes

    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.

    Support Teaching Python

    Episode 160: Data Science, Math and Python, Oh My! Jul 16, 2026
    Show notes

    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.

    Support Teaching Python

    Episode 159: Big Lessons from Small Models with Gwyneth Peña‑Siguenza Jun 22, 2026
    Show notes

    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

    Support Teaching Python

    Episode 158: Will Vincent on Django, AI Coding, and Why Fundamentals Still Matter Jun 10, 2026
    Show notes

    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
    A look at why Django continues to be a strong choice for building production applications, even if it doesn't receive the same level of attention as newer frameworks.

    The Reality Behind "Vibe Coding"
    Exploring the gap between generating code with AI and understanding the systems, tradeoffs, and architecture required to build reliable software.

    Learning to Program as an Adult
    Will reflects on his path from book editing and startup leadership to becoming a self-taught programmer, educator, and author.

    AI and Programming Education
    A discussion about how AI changes the learning process, why fundamentals still matter, and how concepts like music theory can help explain the value of understanding code beneath the surface.

    The Growing Burden on Open Source
    How maintainers are dealing with an influx of low-quality AI-generated issues, pull requests, and content, and what that means for community-driven projects.

    Local and Specialized AI Models
    Why privacy concerns, lower inference costs, and better hardware may drive adoption of smaller, task-focused models rather than ever-larger general systems.

    Developer Concerns in the AI Era
    How engineers are responding to growing pressure from leadership teams eager to adopt AI, and what trends JetBrains is seeing across the developer ecosystem.

    Resources Mentioned
    LearnDjango, Will Vincent's platform for learning Django and web development.
    Hello World 5 Different Ways, a Django tutorial that introduces key concepts through practical examples.
    Django Chat, the podcast Will co-hosts covering the Django ecosystem and web development.
    Django News, a weekly newsletter highlighting updates from the Django community.
    JetBrains, the software development company behind tools such as PyCharm and IntelliJ IDEA.

    Special Guest: Will Vincent.

    Support Teaching Python

    Episode 157: Philip Guo: The Code Runs. But Do You Understand It? May 30, 2026
    Show notes

    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
    *
    Philip: Hiring a second undergraduate student for Python Tutor, including one focused on user experience research with K-12 teachers
    Kelly: Finishing a year of in-person teacher trainings and reflecting on how far the teachers have come

    *AI, Coding, and Classroom Understanding
    *
    Much of the conversation focuses on how AI-generated code affects student learning. Kelly describes using AI code with eighth graders and how difficult it can be for them to understand functions, parameters, returns, and other fundamentals when the code is generated all at once. Philip suggests that tools like Python Tutor may be useful for helping students trace code and understand what is happening behind the scenes.

    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
    **0:25 Python Tutor and AI Learning
    1:55 Hiring Help for Python Tutor
    4:07 Classroom Wins and AI Reflections
    6:11 Teaching Code Through Python Tutor
    9:03 AI Code and Student Confusion
    14:11 Simplifying Execution Traces
    17:19 Functions Are the Hard Part
    20:25 Keeping Fundamentals in AI Era
    24:25 Socratic Seminars for Code
    26:27 Voice-Based Code Thinking
    29:27 Learning Beyond Lockdown
    36:10 Prompting as a New Skill
    36:25 Hardware Troubles and NeoPixels
    40:15 Beyond the Code Editor
    45:01 New Research on Embodied AI
    49:12 PyCon and Community Plans
    50:42 Teacher Call to Action

    Special Guest: Philip Guo.

    Support Teaching Python

    Episode 156: When Code Leaves the Screen May 23, 2026
    Show notes

    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.

    Support Teaching Python

    Episode 155: Hello World is Dead Apr 06, 2026
    Show notes

    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.

    Key Topics

    • Why "Hello, World" no longer delivers the same dopamine hit for new learners
    • The three types of student responses to AI-assisted coding
    • Using AI to write deterministic code vs. using generative AI for repetitive tasks
    • Sean's Python + SQLite org design tool as a real-world "solve a problem with code" example
    • Kelly's classroom experiments with AI-generated Python apps for 8th graders
    • EarSketch and making music with Python as a reliable engagement tool
    • Whether AI can remove administrative clutter and let teachers focus on pedagogy
    • The concept of "desirable difficulty" in learning
    • Bridging the knowledge gap: helping non-coders see code as a problem-solving option
    • PyCon US Education Summit — May 14, 2026
    • Julian Sequeira joining as a regular co-host

    Wins of the Week

    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.

    Announcements

    • PyCon US Education Summit — Thursday, May 14, 2026 in Pittsburgh. Kelly is chairing the summit with 150–200 seats available. Proposals are open and encouraged.
    • Julian Sequeira joins Teaching Python — After almost 8 years as a duo, Sean and Kelly have invited Julian to be a regular co-host, bringing fresh perspective, energy, and an Australian accent to the show.

    Resources & Links

    • Teaching Python — Podcast website
    • PyBites — Julian Sequeira's Python coaching platform
    • EarSketch — Making music with Python (Georgia Tech)
    • PyCon US 2026 — May 14–22, 2026 in Pittsburgh, PA
    • Claude Code — AI coding assistant mentioned by Kelly

    Support Teaching Python

    Episode 154: Are You Techie Enough? Mar 03, 2026
    Show notes

    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.

    Show Notes

    Wins of the Week

    • Amelia: Getting both kids to all their activities this week — taekwondo, Chinese language classes, and a piano competition where her oldest did very well
    • Kelly: Running a series of well-attended trainings at school, including a Canva AI session that drew 60 attendees across two campuses, with new audiences (kindergarten and first grade teachers) showing up for the first time
    • Sean: Finally getting fiber internet installed at his house after over a decade of waiting — a major upgrade from cable with latency dropping from 20-30ms to 3ms, at half the cost

    Links & Resources Mentioned

    • vBrownBag — Tech community show that Amelia is preparing to present at and Sean is scheduled for later in the year
    • PyCon US 2025 — Pittsburgh, May 2025; Education Summit on Thursday, May 14
    • LEGO Mindstorms — Referenced in Amelia's story about building a vending machine in 4th grade
    • Architects of Intelligence — Book Kelly is currently reading (dense but informative, structured as short stories/interviews)
    • How to Winter by Kari Leibowitz — Book Amelia is reading about mindset and how people approach difficult things
    • Lars von Trier / Bjork / Catherine Deneuve film — Referenced in Amelia's story about visiting a film set in Denmark at age 18 (the film Dancer in the Dark, 2000)
    • Chris Williams / vBrownBag — Mutual connection who introduced Sean and Amelia at AWS re:Invent

    Announcements

    • PyCon US 2025 — Pittsburgh, PA. Education Summit is Thursday, May 14. Proposals still open at time of recording.
    • Kelly will be attending PyCon with her youngest son, who will spend the weekend with family at Disneyland
    • Sean will be supporting from home this year as his wife has a conflicting travel commitment

    Key Quotes

    "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.

    Support Teaching Python

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