Computer Science I · Fall 2026 · McDonogh 35
Course Overview
Computer Science I is about directing AI to build things you couldn’t build by hand — and then reading what it made, judging whether it’s right, and making it better. You will not be asked to write code from scratch. You will be asked to be the author of what you make.
What this course is
Most people meet a computer as a service — a set of buttons that promise to solve your problem if you click the right ones. This course treats it as something you can direct. The work is not memorizing syntax; it is learning to say precisely what you want, read what the machine makes of it, and tell the difference between output that is right and output that only looks right.
Four verbs run through everything you do here: directing a tool with a clear specification, reading what it produces, judging whether it meets the spec and holds up, and iterating — correcting the specification rather than patching the output. The spec is the work; the code is a by-product.
What you’ll walk out with
- The habit of auditing — checking a source, a dataset, or an AI's output before you trust it, and before you pass it on.
- A specification you wrote, and a working thing built from it, deployed and live at a public web address.
- An SDD Portfolio: your specifications, what the machine generated, the audits you ran on it, and an honest account of what was yours.
- A working mental model of how a program runs — enough to read a segment of code and predict what it does.
- The disposition of an author rather than a consumer: you direct the tool, you don't hand your thinking to it.
How it works
The semester runs as three six-week modules — reading the machine, authoring it, and understanding how programs work. Each class block splits into a short content segment and a longer lab, both run as a model / practice / do-it-yourself loop. The day-to-day lives on the calendar; the full structure and the standards behind each module live on the roadmap.
Expectations & the room
The working agreements for this lab aren’t handed down — we build them together through the first activities of the course. Two pages hold the starting points:
Grading
To be finalized
The grading breakdown — how unit assessments, the pre/post growth instrument, and the SDD Portfolio combine into a term grade — is being finalized and will be published here before the term opens.
Materials
To be finalized
The materials list for the course — what you need, what the lab provides, and what stays in the room — is being finalized and will be published here before the term opens.
Syllabus
The full course audit — how Computer Science I aligns to the standards and the readiness it’s built for — is documented separately.
📄 Course Audit & Syllabus