Turning Projects into Pedagogy: An Interview with Artmink Creator Brian McClain

We sat down with Brian to explore how his personal projects drive innovation in Noble's AI and programming curriculum, what he hopes students achieve in his courses, and why coding transcends mere syntax—it's about solving real problems that matter.

Q: As a lead course developer, how do you decide which technologies or skills to include in your curriculum to ensure it stays relevant and industry-aligned?

I've increasingly been looking toward my own personal project, Artmink, for guidance on what curriculum to develop next. There's no substitute for hands-on experience when it comes to understanding which technologies actually deliver value in production environments. When you're building real applications that solve genuine problems, you quickly learn which tools are essential and which are just hype.

Q: That's right. You're currently working on Artmink, an app that uses generative AI for visual analysis, helping people evaluate antiques. How has your experience building that shaped your perspective as a teacher and as a user of AI?

My work as an AI/ML Engineer and app developer has fundamentally transformed how I teach. I don't just know more—I know what matters more. When you've wrestled with API limitations at 2 AM or debugged model performance issues in production, you understand which concepts students truly need to master versus what they can learn on the job. As for AI taking jobs, I've come to realize it's not AI that's going to take your job—it's the professional who effectively leverages AI who might. The technology amplifies capability; it doesn't replace critical thinking.

Building Artmink has also given me deep insights into the practical challenges of AI implementation: data quality issues, model bias, computational costs, and user experience design for AI-powered features. These real-world experiences directly inform how I structure learning objectives and project work in my courses.

Q: What do you hope students walk away with after completing one of your courses, especially those new to programming or data?

Regardless of their incoming experience level, I want students to leave with genuine confidence and a tangible sense of achievement—proof that they've "leveled up" their capabilities in measurable ways. But beyond technical skills, the crucial insight I hope they internalize is that programming success isn't about memorizing syntax or frameworks. It's about developing strong analytical thinking and systematic problem-solving approaches.

In today's rapidly evolving tech landscape, the specific tools and languages will continue to change. What remains constant is the ability to break down complex problems, think logically about solutions, and adapt to new technologies. Students who master these meta-skills become resilient, valuable professionals regardless of which direction the industry moves.

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Q: How do you introduce students to real-world applications of AI, especially in your machine learning and Python courses?

In both the Python for Machine Learning and Python for AI Apps courses that I developed for Noble, AI applications are woven throughout the curriculum rather than treated as abstract concepts. Students don't just learn about AI—they build functioning AI-powered applications from day one.

In the AI Apps course, we develop a sophisticated chatbot and an image analysis application directly inspired by Artmink's architecture. These aren't toy projects; they're scaled-down versions of production systems that students could genuinely deploy. The technical stack we work with—JavaScript frontend communicating with Python Flask backends, integrated with OpenAI's API—mirrors what students will encounter in professional development environments.

This approach ensures students understand not just how to call an AI API, but how to architect complete systems around AI capabilities, handle error cases, manage API costs, and design user experiences that make AI functionality accessible and valuable.

Q: Are there any tools, platforms, or libraries you've found particularly effective in teaching applied AI or data science?

For AI application development, the OpenAI API remains a cornerstone, though we also explore other platforms to give students a broader perspective on the ecosystem. The key is teaching students to evaluate and integrate multiple AI services rather than becoming dependent on a single provider.

For machine learning model development, we focus heavily on TensorFlow-Keras in our foundational ML course, then advance to PyTorch in our second-level curriculum. This progression gives students experience with both major frameworks while understanding when to choose each. TensorFlow excels for production deployment and scalability, while PyTorch offers more intuitive research and experimentation workflows.

I've also integrated newer tools like Hugging Face Transformers and LangChain, which have become essential for modern AI development. Students need to understand how to leverage pre-trained models effectively rather than building everything from scratch.

Q: Do you use AI-based tools in your teaching, and if so, how have students responded to them?

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Students have responded enthusiastically—enough so that my Python for AI Apps class inspired me to create a custom edit of the "Are You Not Entertained?" scene from Gladiator, showing the protagonist conquering entire tech stacks instead of gladiators. It's become something of a class tradition.

More seriously, I leverage AI tools not as substitutes for learning, but as accelerators that help students focus on higher-level concepts. We use AI-assisted code generation to prototype quickly, then dive deep into understanding and optimizing what we've built. This mirrors how professional developers actually work in 2026—using AI to handle routine tasks while applying human insight to architecture, strategy, and creative problem-solving.

The goal is teaching students to be AI-native developers: professionals who seamlessly integrate AI tools into their workflow while maintaining strong foundational knowledge and critical thinking skills.

You can view the gladiator scene in question here.

Curious about Artmink: learn more and download the app for free.

Explore Noble Desktop's Python classes and AI classes to gain real-world skills and discover how Brian's innovative, project-driven approach brings cutting-edge AI development practices directly into the classroom.