OIM3690 AI-Powered Web Development, Summer 2026 Syllabus
Class Information
- Format: Fully online via WebEx
- Schedule: Monday and Wednesday, 11:30 AM to 1:30 PM ET (120 minutes)
- Dates: Monday, 5/18 to Wednesday, 7/22, 2026 (10 weeks, 19 sessions)
- Final submission deadline: Friday, 7/24
Instructor
- Instructor: Dr. Zhi Li
- Email: zli@babson.edu
- Office Hours: WebEx by appointment. Email me to schedule a time.
Required 1:1 Meeting: Every student must complete one 15-minute WebEx meeting with the instructor during the semester. This is a chance to discuss your progress, ask questions, and get personalized feedback. Email me to schedule whenever you are ready. This meeting counts toward your participation grade.
Course Description
This course prepares students to build web products in the AI era by focusing on how to collaborate effectively with AI tools rather than memorizing syntax. From the first day, students use AI to generate complete websites and progressively learn to understand, evaluate, modify, and improve AI-generated code. Core web technologies including HTML, CSS, and JavaScript are introduced as essential concepts for guiding AI systems and making informed design and technical decisions. Through a single evolving project, students experience the full lifecycle of modern web development, from AI-assisted prototyping and iterative refinement to version control, deployment, and ongoing improvement in real-world web environments. By the end of the course, students will be able to use AI responsibly to build and maintain web applications while retaining the conceptual understanding needed to debug issues, assess tradeoffs, and shape AI-generated solutions to meet real product goals.
Learning Objectives
By the end of this course, students will be able to:
- Direct AI tools to generate, explain, debug, and refactor web code
- Read and evaluate AI-generated HTML, CSS, and JavaScript critically
- Build responsive, interactive web applications with live deployment
- Use Git and GitHub for version control and public portfolio building
- Explain fundamental web concepts including client-server architecture, the DOM, responsive design, and APIs
- Integrate external APIs and handle asynchronous data fetching
- Deploy and maintain live web applications on GitHub Pages
Prerequisites
Students should have basic proficiency with a personal computer, including the ability to use a web browser, navigate files, and install software. No prior programming experience is required.
Textbook
There is no required textbook for this course. All materials will be provided via Canvas and GitHub. The following references are especially useful and will be referenced throughout the course:
- MDN Web Docs, the authoritative reference for HTML, CSS, and JavaScript
- W3Schools, beginner-friendly tutorials and examples
Software and Tools
Required (all free)
| Tool | Purpose |
|---|---|
| Visual Studio Code | Code editor |
| GitHub account (sign up with Babson email) | Version control and deployment |
| GitHub Copilot Free | AI coding assistant (2000 inline completions + 50 chat messages per month, included with any GitHub account) |
| GitHub Desktop | Git GUI for committing and pushing code |
| Google Chrome | Primary browser with DevTools |
| Claude.ai (free tier) | AI assistant for learning and coding |
| ChatGPT (free tier) | AI assistant (backup and comparison) |
No paid tool is required to complete any assignment in this course. Students are encouraged to apply for the GitHub Student Developer Pack, which provides free access to Copilot Pro and a free domain name, but approval takes 1 to 3 weeks and is not guaranteed.
Recommended (not required)
- Claude Code, a terminal-based AI coding agent (requires API key; demonstrated in class, not required for assignments)
- OpenAI Codex, an autonomous coding agent from OpenAI (similar to Claude Code; demonstrated only)
These tools represent where the industry is heading. You will see them demonstrated in class, but you are not expected to use them for your own work.
Grading
| Component | Weight | What it measures |
|---|---|---|
| Checkpoints (5) | 50% | Are you building, deploying, and learning? |
| Final Project | 30% | Can you build something independently? |
| Participation | 20% | Are you engaged and present? |
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 94 to 100 | C+ | 77 to 79.99 |
| A- | 90 to 93.99 | C | 74 to 76.99 |
| B+ | 87 to 89.99 | C- | 70 to 73.99 |
| B | 84 to 86.99 | D | 60 to 69.99 |
| B- | 80 to 83.99 | F | Below 60 |
Checkpoints (50%)
Your learning progress is tracked through five checkpoints, one every two weeks. Each checkpoint is a holistic review of your work including repo structure, weekly logs, project deployments, and your understanding of the code you have submitted.
| CP | Due | General focus |
|---|---|---|
| 1 | Sunday, 5/31 | Environment setup, first logs |
| 2 | Sunday, 6/14 | Project 1 progress |
| 3 | Sunday, 6/28 | Project 2 progress |
| 4 | Sunday, 7/12 | Project 3 progress, final project proposal |
| 5 | Friday, 7/24 | Final project, all logs complete |
Specific requirements for each checkpoint will be announced on Canvas before its due date so that the instructor can adjust expectations based on actual class progress.
How checkpoints are evaluated:
- Structure checks on repo structure, log format, and deployment status.
- Review of deployed sites, README quality, and commit history.
- Oral interviews: the instructor may ask you to walk through your code and explain what it does, either during class or in a 1:1 meeting. You should be able to open DevTools, point to any part of your submission, and explain it.
- In-class presentations: you may be asked to demo your project to the class (2 minutes plus Q&A). This is a conversation, not an exam.
Checkpoints evaluate completeness and process, not subjective code quality or visual design. The goal is to verify that you are building real things, deploying them, and understanding the code in your submissions.
Three Mini Projects
Over the semester you will build three progressively complex web projects. Each project lives in a separate public GitHub repo and is deployed on GitHub Pages:
| # | Project | Core Skills | Released | Due |
|---|---|---|---|---|
| 1 | Website for Someone Else | HTML, CSS, interviewing, iteration | Wednesday, 5/27 | Thursday, 6/12 |
| 2 | Interactive Web Tool | JavaScript, DOM, events, state | Wednesday, 6/10 | Friday, 6/26 |
| 3 | API-Powered App | fetch, async/await, JSON, APIs | Wednesday, 6/24 | Friday, 7/10 |
Mini project progress is reviewed as part of your checkpoints. Each project may include a showcase demo where you present your live site to the class, and the class votes on a category award (top 3 earn +1 bonus each).
Final Project (30%)
After completing three mini projects, you will have the skills and experience to build something of your own. The final project is an independent web application that you propose, build, and deploy.
| Milestone | Date |
|---|---|
| Proposal due | Sunday, 7/12 |
| Demo Day | Monday, 7/20 |
| Final submission | Friday, 7/24 |
The final project must be a functional web application (not just static content), deployed with a live URL, and documented with a README that explains what it does, how to use it, and what you learned building it.
Participation (20%)
Participation is evaluated through observable engagement, not just attendance:
- In-class Q&A and discussion: contributing to class conversations, asking questions, and responding to instructor prompts.
- Live-coding cold calls: each session, 2 randomly selected students screen-share and make a small change to their code (about 5 minutes each). These are scored on process and willingness to engage, not on whether your code is perfect.
- Required 1:1 meeting: one 15-minute WebEx meeting with the instructor at any point during the semester.
- Weekly log quality: consistent submission of well-structured weekly logs throughout the semester.
Weekly Learning Logs
Each week, you will submit a brief learning log in your course repo (logs/wk01.md through logs/wk10.md). The log is evidence of your work, not a reflection essay. It should take about 5 minutes to write.
Required template (4 sections):
- What I worked on this week: 1 to 2 sentences linking to your repo or feature.
- Code I want to highlight: a fenced code block (2 to 15 lines) of code you wrote and can explain.
- AI usage this week: what you asked AI to do, what it gave you, and what you changed about it.
- One question I still have: an open-ended question a teacher or classmate could help with.
A log missing any section or with an empty code block is incomplete.
Your logs are not graded on length or eloquence. They are graded on whether they exist, follow the template, and contain real content. The AI usage section is the most important part because it documents your process of working with AI, which is the core skill of this course.
Build in Public
All coursework lives in public GitHub repositories. This builds your portfolio from Day 1.
| Repository | Purpose | URL |
|---|---|---|
username.github.io |
Personal website and portfolio | https://username.github.io |
oim3690-summer |
Course repo (exercises, logs) | https://username.github.io/oim3690-summer |
[project-name] |
Each mini project in its own repo | https://username.github.io/[project-name] |
[final-project] |
Final project | https://username.github.io/[final-project] |
You must frequently commit and push your work. Your commit history is part of your submission and should tell the story of how your project evolved over time. A project with a single commit dumping all the code does not demonstrate the iterative process this course requires.
AI Tool Policy
AI tools are encouraged and expected in this course from Day 1.
- Use AI freely: Copilot, Claude, ChatGPT, v0.dev, and any other AI tool are permitted on every assignment.
- Understand what you submit: You must be able to explain the purpose and behavior of every piece of code in your project. If the instructor asks "what does this line do?", you should be able to answer.
- Document AI usage: Section 3 of your weekly log captures what AI did and what you changed about its output.
- No tool is required: Grading is tool-agnostic. You will never be penalized for using or not using a specific AI tool.
Using AI to generate code you don't understand is not learning. It is the one thing this course considers academic misconduct. The demos, cold calls, and checkpoint interviews exist to verify your actual understanding.
Small Groups
Students are organized into small groups that rotate every few weeks so that everyone works with different classmates over the semester. Groups meet in WebEx breakout rooms during class for collaborative tasks such as code walkthroughs, debug pairing, AI output critique, and peer review. Group membership is not graded. It is a learning structure, not an assessment unit.
Camera Policy
Cameras must be on during all live sessions. This is an online course, and visible presence is essential for meaningful interaction, cold calls, demos, and group work. If you have a specific reason you cannot turn on your camera for a particular session, email the instructor before class to request an exception.
Schedule Overview
19 sessions (no class 5/25, Memorial Day). See the course website for the full schedule.
| # | Date | Topic | Deliverable |
|---|---|---|---|
| 1 | Monday, 5/18 | Course intro, GitHub + AI setup | |
| 2 | Wednesday, 5/20 | How the web works, first deploy | |
| — | Monday, 5/25 | No class (Memorial Day) | |
| 3 | Wednesday, 5/27 | HTML essentials, Reading AI code, MP1 launch | CP1 due Sunday, 5/31 |
| 4 | Monday, 6/1 | CSS essentials: selectors, cascade | |
| 5 | Wednesday, 6/3 | Box model, Flexbox, MP1 mid-demo | |
| 6 | Monday, 6/8 | Responsive design + Forms, JS intro | |
| 7 | Wednesday, 6/10 | JS variables, types, functions, MP2 launch | CP2 due Sunday, 6/14 |
| 8 | Monday, 6/15 | MP1 showcase, JS conditionals, AI debugging | |
| 9 | Wednesday, 6/17 | JS arrays + loops, AI tool comparison | |
| 10 | Monday, 6/22 | JS async + fetch, API keys + safety | |
| 11 | Wednesday, 6/24 | AI code review, MP3 launch | CP3 due Sunday, 6/28 |
| 12 | Monday, 6/29 | MP2 showcase, localStorage, Claude Code intro | |
| 13 | Wednesday, 7/1 | Project velocity day | |
| 14 | Monday, 7/6 | Flex topics + deployment troubleshooting | |
| 15 | Wednesday, 7/8 | MP3 showcase, peer code review | CP4 due Sunday, 7/12 |
| 16 | Monday, 7/13 | Final project work + dress rehearsal | |
| 17 | Wednesday, 7/15 | Final project work + peer review | |
| 18 | Monday, 7/20 | Demo Day (Final Project) | |
| 19 | Wednesday, 7/22 | Course wrap-up | CP5 / Final due Friday, 7/24 |
Course Policies
Attendance
This course meets live on WebEx and attendance is expected at every session. WebEx automatically records join and leave times so there is no manual roll call. Your attendance record is not graded directly, but consistent absence affects your participation score because you miss cold calls, group work, and demo opportunities that cannot be made up.
If you will be absent, please notify the instructor by email before the session. Three or more unexcused absences will trigger a mandatory advising conversation with the instructor. Five or more unexcused absences will result in a participation score of zero and may lead to a referral to the program office.
Babson-sanctioned absences such as job interviews, athletic competitions, religious observance, and documented illness do not count against you, provided you notify the instructor in advance or within 48 hours after the absence.
Recordings
All sessions are recorded and the recordings are posted to Canvas within 24 hours. Recordings are provided so that absent students can catch up on content, but watching a recording does not substitute for the interactive parts of class such as cold calls, breakout work, and demo presentations. If you miss a session, you are still responsible for completing any exercises or logs from that session.
Missed Work and Late Submissions
Checkpoint deadlines are firm. Late submissions receive reduced credit at the instructor's discretion. If you are facing circumstances that prevent you from meeting a deadline, contact the instructor before the deadline to discuss options. Extensions are granted on a case-by-case basis and require advance communication.
Academic Integrity
All work must comply with the Babson College Undergraduate Honor Code. In this course, AI assistance is explicitly permitted and encouraged. However, submitting work that you cannot explain constitutes academic misconduct. If you are asked to walk through your code and cannot explain what it does or why it is there, that is treated the same as submitting someone else's work as your own.
Collaboration with classmates is encouraged for learning. Discussing ideas, helping each other debug, and reviewing each other's code are all positive behaviors. Copying another student's code without attribution or understanding is not.
Need for Academic Accommodation
If you need academic accommodations due to a documented disability, please contact the Department of Accessibility Services (DAS) at accessibility@babson.edu or 781-239-5509. Please reach out to DAS as early as possible so that accommodations can be arranged before they are needed.
Need for Religious Accommodations
Babson College is committed to supporting students who observe religious holidays. If a religious observance conflicts with a class session or deadline, please contact the instructor within the first two weeks of the semester so that alternative arrangements can be made.
Communication
The primary communication channels for this course are Canvas (for announcements, grades, and session recordings) and GitHub (for code and learning logs). For private matters, email the instructor at zli@babson.edu. When emailing, please include the course number in the subject line (for example, "OIM3690: Question about CP2") so that your message can be identified and prioritized.
Expect a response to emails within 24 hours on weekdays. Weekend emails will be answered by the following Monday.
Updated: 2026-05-12