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.

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.

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.

• 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

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.

• 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.

• 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