AI has become a normal part of learning technical subjects, and ICS 314 was no exception. Even though we weren’t required to use AI tools, I regularly used ChatGPT throughout the semester because it fit naturally into how I learn. Tools like GitHub Copilot or Bard exist, but ChatGPT felt the most helpful for understanding errors, breaking down problems, and getting quick explanations when I got stuck.
This essay explains how I used AI in ICS 314 and how it shaped my learning experience.
I used AI the most during WODs, the final project, and when learning new tools.
During Experience WODs, AI helped me understand what the instructions were actually asking for. Sometimes I felt unsure about how to even start a problem, so I would ask general questions like how a certain function worked or what the WOD was really focusing on. This helped me understand the overall goal before writing any code.
For Practice WODs and in-class graded WODs, I used AI in a more limited way—mainly for small clarifications, such as confirming syntax or understanding why something wasn’t working the way I expected. Under time pressure, these quick checks helped me keep moving without getting stuck on tiny mistakes.
During the final project, AI became even more useful because many tools interacted at once. When Prisma migrations failed or Next.js behaved unexpectedly, I often asked:
AI helped me understand the errors instead of just copying fixes. It made documentation easier to read and helped me avoid breaking the database or deployment environment.
AI also helped when I needed high-level explanations of Prisma, NextAuth, GitHub Actions, and other tools. It made it easier to understand why certain steps in the documentation mattered instead of just memorizing commands.
I also used AI for communication tasks such as organizing essays, outlining reflections, and cleaning up documentation. For example, when I needed to write an effort estimation essay, I asked:
AI helped me structure my thoughts so I could focus on the content itself. It also helped me write clearer technical messages to teammates and instructors.
One of the most valuable uses of AI is debugging. I often asked:
This saved a lot of time and helped me learn from mistakes instead of getting stuck.
AI helped me stay productive and move past roadblocks faster. It freed up time so I could focus on understanding concepts instead of getting stuck on small errors. At the same time, it reminded me to double-check answers, because AI can sound confident even when it’s wrong.
Overall, AI supported my learning without replacing the need to actually understand the material.
Outside ICS 314, I used AI in other computer engineering courses to help debug code or learn new topics. The experience was similar: AI works best as a support tool, not a replacement for real learning.
The biggest challenge with AI is that it can occasionally give wrong or incomplete answers. This makes verification important. Compared to traditional learning, AI is faster and more flexible, but traditional approaches still give a deeper understanding.
Combining both gave me the best results.
Going forward, I think AI will become an even bigger part of computer engineering education. Clear guidelines on responsible use would make sure students get the most out of it without depending on it too much.
AI played a helpful role throughout my time in ICS 314. It made debugging easier, helped me understand new tools, and saved time during the final project. When used intentionally, AI can strengthen both learning and productivity, especially in a class that involves real software engineering techniques.