Kellon Petzak

From this module, I learned that integrating AI into teaching isn't about replacing my expertise but about using AI strategically for tasks like generating variations, structuring outlines, and adapting content while keeping professional judgment at the center. I intend to start with a single, low-stakes task, like creating multiple practice problem variations or drafting lesson outlines

Curriculum updates and learning sequence design require your industry expertise to weigh what's valuable against what's obsolete, while the team approach with AI helps you explore integration options and maintain logical progressions. Authentic assessment and skill integration similarly benefit from collaborative thinking.

I learned that AI's greatest value in education comes from pattern-based, repeatable tasks like generating examples, outlines, or variations, but your professional judgment remains essential for guiding direction and verifying quality. The key is to start with low-risk, measurable tasks where you can easily spot errors, then gradually expand as you build confidence and refine your process.

This example reinforces a key lesson for me: when integrating AI into a workflow, starting small and focused is more effective than trying to tackle everything at once. By choosing a low-stakes, measurable task like vocabulary exercises, Jack was able to learn quickly, refine his process, and see immediate value without risking student outcomes. 

Ultimately, AI is not just enhancing technology in CTE, it is transforming how technical education is delivered, preparing students for a workforce that is increasingly driven by AI and automation, while emphasizing inclusive and ethical learning practices. The integration of AI with augmented and virtual reality will create highly immersive, hands-on learning environments, while predictive analytics will help educators identify and support at-risk students earlier.

Studying these AI concepts has given me a much clearer understanding of how rapidly the field is evolving and how different techniques work together to solve complex problems. I now see AI not just as advanced technology, but as a set of practical tools that can be applied across many industries.

As someone who teaches computer science and robotics, I view algorithmic bias not as a minor technical glitch, but as a critical ethical issue that future engineers must learn to identify and address. Students need to understand that every dataset carries the values of the people who created it. When we build robots or AI systems, we are not just writing code — we are encoding decisions that can affect real human lives.

I am currently writing a dissertation on LLM's in education, but this was a good refresher. In this module on the Foundations of AI, I gained a better understanding of how artificial intelligence developed over time and what core ideas it is built upon. I found it interesting to see how the field has evolved from its early beginnings to the powerful systems we have today. I plan to use this knowledge to think more carefully about the AI tools I use in my daily life and studies, and to approach them with greater awareness. I’m curious to hear what… >>>