"CS 1.5": An Experience Report on Integrating CS1 and Discrete Structures for the AI Era
This paper presents an experience report on "CS 1.5," an integrated course at Northeastern University Vancouver that merges introductory programming and discrete mathematics into a studio-based model, leveraging AI as a collaborative tool to shift focus from code generation to comprehension and deep theoretical understanding.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine you are teaching someone to cook.
The Old Way (2015): You make them memorize every spice name, chop every onion by hand, and write out the recipe from scratch. If they can't do it perfectly, they fail.
The New Reality (2025): A super-smart robot chef (Generative AI) can chop the onions, mix the spices, and write the recipe in a split second. If you still force students to chop onions by hand, you aren't teaching them to cook; you're just making them compete with a robot and losing.
This paper is about a group of teachers at Northeastern University who decided to stop fighting the robot chef and instead teach their students how to be Master Chefs in a world full of robots. They created a new class called "CS 1.5" (a playful name meaning "between the basics and the next level").
Here is the story of what they did, explained simply:
1. The Big Problem: The "Robot Chef" is Here
In the past, computer science classes focused on memorizing syntax (the grammar of code) and writing small programs from scratch. But now, AI can write that code instantly.
- The Fear: Students might just ask the AI to do the work and never learn how to think.
- The Realization: The teachers realized that if AI can write the code, the student's job isn't to be a typist; it's to be an architect and a critic. They need to understand why the code works, not just how to type it.
2. The Solution: Merging Two Worlds
Usually, schools teach "How to Code" (CS1) and "Math Logic" (Discrete Structures) as two separate classes, often months apart. It's like teaching someone how to drive a car in one class and teaching them the laws of physics in another, without ever connecting the two.
The teachers realized these two subjects are actually mirror images of each other.
- Recursion (a coding loop that calls itself) is the same thing as Mathematical Induction (proving a pattern works forever).
- Sets (groups of items in math) are exactly like Lists (groups of items in code).
The Experiment: They smashed these two classes together into one giant, 4-hour "Studio" block. They called it CS 1.5.
3. How They Taught: The "Playground" Approach
Instead of sitting in rows listening to lectures, the class felt more like a workshop or a playground.
- The "Sharing Circle": Before coding, everyone sat in a circle and shared stories. A student might talk about navigating a corn maze, and the teacher would use that story to explain how computers find paths (Graph Theory). It turned abstract math into human stories.
- Dancing Bubble Sort: To understand how computers sort things, the students didn't just watch a video. They stood up, held numbers, and danced to Hungarian folk music to physically act out a sorting algorithm. They felt the "swaps" and "comparisons" with their bodies.
- The "Unplugged" Rule: Sometimes, they weren't allowed to touch computers. They had to draw on whiteboards or use wooden nesting dolls (Matryoshka dolls) to understand how recursion works. You have to understand the idea before you let the machine do the work.
4. The Projects: Reading, Not Just Writing
In a normal class, students write small scripts (50 lines of code). In CS 1.5, the students were given massive, messy codebases (2,000+ lines) that were already written.
- The Task: Their job wasn't to write everything from scratch. It was to read the code, figure out how the pieces fit together (like solving a giant puzzle), and fix or add specific parts.
- The AI Role: They were encouraged to use AI tools to help them read and understand the complex code. The goal was: "Use the robot to help you navigate the forest, but you must know where you are going."
- Real-World Examples:
- Set Theory: They built an app where you draw a Venn diagram, and the AI analyzes it to check your math.
- Recursion: They built a strategy game where the cost of a move was calculated using math proofs. If your math proof was wrong, the game wouldn't let you move.
- Probability: They built a Dungeons & Dragons combat simulator to prove that math formulas actually work in real life.
5. The Final Test: The "Code Walk"
They stopped giving traditional exams. Instead, every student had to do a "Code Walk."
- They had to stand in front of a peer and a professor and explain their code line-by-line.
- The professor would ask, "What happens if I click this button?" or "What if we change this number?"
- If the student couldn't explain why the code worked, they failed. It didn't matter if the code ran; if they didn't understand it, it was useless. This proved they weren't just copying the AI; they were the masters of the machine.
6. The Result: A New Kind of Teacher
The teachers realized their role changed. They weren't the "Sage on the Stage" (the person with all the answers). They became Mentors and Navigators.
- The AI could give the answer, but the teacher helped the student figure out which answer was right for the problem.
- The students formed a tight-knit community. By the end, they weren't just classmates; they were a team of problem-solvers who trusted each other.
The Takeaway
The paper argues that in the age of AI, we shouldn't ban the tools. Instead, we should change our schools to focus on understanding, logic, and human connection.
- Don't teach students to be typists (AI is better at that).
- Do teach them to be architects who can design systems, spot errors, and understand the deep logic behind the magic.
"CS 1.5" was a risky experiment, but it showed that when you mix math, coding, and human storytelling, you can create computer scientists who are ready for the future, not just the past.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.