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.