How I use AI to learn engineering (anti brain rot studying)

Summary

Alex Gornik argues that AI can help engineering students learn difficult topics faster—not by doing the work for them, but by making concepts easier to visualize, filtering out low-value material, helping them get unstuck, and finding useful learning resources.

Gornik introduces himself as an electrical engineering graduate with experience in AI, hardware, and software engineering. He now works at an AI startup and shares four ways he uses AI in his learning and work.

1. Turn abstract ideas into interactive visuals

AI can create visual explanations and models for subjects that are difficult to understand through text or technical documentation alone. For example, you can:

• Ask an AI coding tool for several robotics project ideas, have it explain which one it recommends, and ask it to create an interactive presentation with a 3D model.
• Upload a photo of electronic equipment and request a step-by-step visual guide to wiring it—for example, connecting an STM32 board to an ST-LINK for power measurements.
• Use a stronger model for 3D or spatial tasks, if available, since it may interpret the physical setup more accurately.

Gornik presents visualizations as a useful learning aid, not a complete substitute for learning.

2. Use AI to filter reading lists

Students and professionals often have more books, papers, and tutorials to read than they can reasonably get through. Gornik recommends giving AI relevant context—such as a course syllabus, bibliography, curriculum, or exam requirements—and asking it to identify the most useful materials.

A suggested process:

1. Provide the syllabus and reading list.
2. Ask which books are most relevant to the course and exams.
3. Narrow the selection by asking which chapters are worth reading.
4. Ask which textbook problems or tutorials are most useful to attempt.
5. When comparing papers, ask which is the best introduction to a topic and which is the most credible or reliable.

The goal is to focus on material that supports a specific learning objective rather than trying to read everything.

3. Find the minimum knowledge needed to get unstuck

Learners often do not need to understand an entire field before making progress. While working on his thesis about using graph neural networks to model airflow in the lungs, Gornik felt overwhelmed by the number of concepts involved in deep learning. He made progress by focusing on a smaller task: using PyTorch to pass data through a neural network and run a training loop.

His recommended approach:

1. Describe the topic or assignment you are stuck on, including your deadline.
2. Ask AI which minimum concepts you need to understand to make a first attempt.
3. Focus on that limited set of concepts and find a suitable resource to learn them.
4. Apply what you learn to move forward.
5. Return to the broader theory later, if needed or useful.

The central lesson is that you do not need to master everything before you start.

4. Use AI to find useful learning resources

AI can help locate specific materials that might otherwise take a long time to find. Gornik describes using AI agents to search for CAD resources, including free 3D models and files such as NASA Mars rover and satellite models.

To make this approach more effective:

• Specify exactly what kind of resource you need, such as models, datasets, tutorials, or project files.
• Ask AI to search broadly, potentially using multiple agents.
• Review the links yourself and verify that the resources are legitimate and usable; AI can hallucinate or return inaccurate information.

Overall takeaway

Gornik’s four-part strategy is to make concepts visual, filter out low-value material, learn the small amount needed to make progress, and find the best resources for the task. AI can help focus and accelerate learning, but it does not replace doing the learning yourself.

The video also includes brief promotions for Gornik’s resume template and engineering-student coaching program.

Speakers and sources featured

• Alex Gornik — the video’s narrator and primary speaker.
• 3Blue1Brown — cited by Gornik as an inspiration for visually explaining mathematics.
• Claude Code, Claude, GPT Codex, and ChatGPT — AI tools mentioned as examples.
• Professors, lecture recordings, and YouTube tutorials — learning sources Gornik says he used in the past.