Required One-on-One Meeting: Each student is required to meet with the instructor at least once during the semester for a brief individual check-in. This meeting is an opportunity to discuss your project progress, ask questions, and receive personalized feedback. You may schedule this meeting during office hours or by appointment.
This course teaches students to build web products in the AI era. Rather than focusing on syntax memorization, students will learn to collaborate effectively with AI tools to create functional, responsive, and user-friendly websites. Starting from Day 1, students will use AI to generate complete websites, then progressively learn to understand, evaluate, modify, and improve AI-generated code. The curriculum covers HTML5, CSS3, and JavaScript fundamentals, not as isolated skills to memorize, but as concepts needed to work effectively with AI assistants. Through a single iterative project that evolves throughout the semester, students will experience the full product development cycle: from AI-assisted prototyping to deployment on GitHub Pages. By the end of the course, students will have the skills to leverage AI tools responsibly while maintaining the understanding needed to evaluate, debug, and guide AI-generated solutions.
By the end of this course, students will be able to:
Students should have a basic understanding of operating a personal computer, including proficiency in using web browsers and the ability to navigate and manipulate files. No prior programming experience is required.
This course does not have any required textbooks. All course materials will be provided in PDF, Markdown, or HTML format and made available on Canvas and/or GitHub. The following resources are especially useful:
| Component | Weight | Description |
|---|---|---|
| Checkpoints | 40% | Learning logs, in-class work, mini projects, etc. |
| Final Project | 25% | Independent product, includes peer review |
| Understanding Assessment | 20% | Paper quizzes and in-person interviews |
| Participation | 15% | Learning log submission, office hours attendance, in-class activities, etc. |
| Bonus | up to 3% | Exceptional side projects |
| Grade | Range |
|---|---|
| A | 94-100 |
| A- | 90-93.99 |
| B+ | 87-89.99 |
| B | 84-86.99 |
| B- | 80-83.99 |
| C+ | 77-79.99 |
| C | 74-76.99 |
| C- | 70-73.99 |
| D | 60-69.99 |
| F | 0-59.99 |
This course embraces the “Learn in Public” and “Build in Public” philosophy. You will create public repositories on GitHub for all coursework. This approach:
You will create multiple repositories:
| Repository | Purpose | URL |
|---|---|---|
username.github.io |
Personal Website | https://username.github.io |
oim3690 |
Course learning (exercises, logs) | https://username.github.io/oim3690 |
[mini-project] |
Mini projects (separate repos) | https://username.github.io/[mini-project] |
[final-project] |
Final Project | https://username.github.io/[final-project] |
Repository structures:
username.github.io/ # Personal Website
├── index.html
├── projects.html # Portfolio page (links to all projects)
├── css/
├── js/
└── README.md
oim3690/ # Course Learning
├── index.html # Main page (links to exercises)
├── ex01.html, ex02.html # In-class exercises
├── css/ # Shared styles
├── js/ # Shared scripts
├── logs/ # Learning logs (Markdown)
├── .github/workflows/ # Automated checks
└── README.md
[project-name]/ # Mini Projects & Final Project (separate repos)
├── index.html
├── css/
├── js/
└── README.md
Important: Your personal website (username.github.io) must include a projects.html page that links to all your projects (mini projects and final project). This serves as your portfolio and makes it easy to showcase your work.
You must frequently commit and push changes. All coursework is evaluated based on your repository content and commit history. Your commit history demonstrates your learning journey and iterative improvement.
Your learning progress is tracked through checkpoints. The number and timing of checkpoints will be announced during the semester.
Each checkpoint evaluates:
Evaluation methods:
Starting around Week 9, you will create an independent product in a separate repository. This is your main deliverable for the course.
Timeline:
Requirements:
Collaboration: During development, you will give and receive feedback from classmates to help each other build better products. See the separate Project Instructions for details.
After each class session, submit a brief learning log in Markdown format in your logs/ folder.
File naming: s01.md, s02.md, etc.
Suggested format (use as reference, develop your own style):
# Session X - [Date] - [Topic]
## What I learned today
[Brief reflection on key concepts - focus on understanding, not syntax]
## Code/work I'm proud of (optional)
[Paste a snippet and explain what it does]
## Challenges I faced
[What was difficult? How did I approach it?]
## AI usage (if any)
[What I asked AI for, how I used/modified the output]
## Questions for next time
[What remains unclear?]
Note: Your logs are for your learning first, grading second. Write honestly about struggles and questions - this demonstrates growth mindset.
Evaluation:
Your understanding is assessed through two complementary methods:
Several in-class paper quizzes throughout the semester. These are judgment-focused, not syntax-focused.
Format: Paper-based, no electronic devices
Question Types:
Philosophy: Quizzes test your ability to think about code, not memorize syntax. You should be able to:
Policy:
Specific quiz dates will be announced on Canvas and in the course schedule.
Brief conversations during checkpoint reviews where I ask you questions about your code and projects. This gives you an opportunity to demonstrate your understanding verbally and allows me to assess your learning in a more natural, conversational setting.
These interviews:
AI tools are encouraged and expected in this course. Guidelines:
Remember: AI helps you build faster, but understanding helps you build better.
You can earn up to 3% bonus points through exceptional projects that go significantly beyond course requirements. Bonus projects must be discussed with and approved by the instructor before starting.
Missed Classes: Inform the instructor if you will be absent. You are responsible for obtaining materials and completing exercises for missed classes.
Need for Academic Accommodation: Contact the Department of Accessibility Services (DAS) at accessibility@babson.edu or 781-239-5509 for accommodation requests.
Need for Religious Accommodations: Contact the instructor early in the semester if religious observances conflict with course requirements.
Honor Code: All work must comply with the Babson College Undergraduate Honor Code. While AI assistance is permitted, you must understand and be able to explain all submitted work.
Academic Integrity: Using AI tools as permitted is not a violation. However, submitting work you cannot explain, or misrepresenting your understanding, is academic misconduct.