15-113: Effective Coding with AI

Prerequisites: 15-112 Fundamentals of Programming and Computer Science
Time Commitment: 6 hours/week (3 hours in class + 3 hours outside work)
Semester: Spring 2026
Instructor: Mike Taylor (mdtaylor at andrew.cmu.edu)

Description

This application-focused course will teach students how to effectively combine intermediate programming skills with contemporary AI tools to enhance their software development workflow. Students will explore the capabilities and limitations of current AI coding assistants, experiment with prompt engineering, and collectively develop standards for maintaining code quality, transparency, and ethical integrity in AI-augmented workflows. The course will also feature seminars from AI experts in industry and academia. Through weekly coding projects, students will rapidly build complete applications while balancing creative problem-solving with rigorous quality assurance. A collaborative approach emphasizes peer learning, with students sharing discoveries and contributing to evolving best practices for prompting and evaluating AI-generated code, ensuring proper attribution, and establishing transparent development protocols. These projects are also designed to jump-start students' portfolios for future employment. By completion, students gain practical development experience with AI tools and will contribute to evolving best practices for future courses, learning from both instructors and peers' experiences with these rapidly advancing technologies.

Course Goals: By completion, students gain practical development experience with AI tools and will contribute to evolving best practices for future courses, learning from both instructors and peers' experiences with these rapidly advancing technologies.

The Portfolio Approach

For your first major assignment, you'll build a personal portfolio website using AI (even if you have no prior experience with HTML or web development).

Make it: Throughout the semester, you'll use your creativity and technical strengths along with AI to build a variety of projects using the strategies and tools we discuss in class.
Get feedback: You'll submit these projects for review and critique by adding them to your portfolio.
Take it with you: By the end of the semester, you'll have a complete online showcase of your AI-augmented development skills.

Add your work from other courses and projects and you'll be able to show off your best work for future internships, jobs, and graduate school applications.

This course jumps into AI-assisted programming from Day 1. We'll alternate between class discussions and regularly building cool stuff, with the goal of finishing the semester with refined strategies for using AI effectively and responsibly.

Guest Lectures

Throughout the semester, we'll host guest lectures from experts in computer science, software engineering, and machine learning/AI research. These engineers and researchers will share:

How they use AI tools in their daily work (coding, debugging, communication, and research)
Real-world applications and case studies from their organizations
Challenges and limitations they've encountered with AI-assisted development
Where they see the technology heading and how it will shape our careers
Advice for students entering the field in the AI era

Tentative Schedule: The schedule of topics presented below is tentative, and may shift to accommodate our guest lectures. Specific dates and speakers will be announced as confirmed. Ideally these will mostly occur during class time, though occasionally we may have optional presentations outside of our normally-scheduled sessions.

Learning Objectives

AI as Force Multiplier: Modern AI tools can be very powerful, but to use them effectively requires us to combine the speed and generative capabilities of AI with our critical thinking and problem-solving skills. This course teaches you how to strategically leverage AI tools to enhance your productivity and versatility while maintaining code quality and strong ethical standards.

At the end of the course, students should be able to:

Build ambitious projects faster by using AI tools strategically while maintaining code quality
Evaluate AI-generated content critically for correctness, security, performance, and maintainability
Choose appropriate AI tools based on task requirements, tool capabilities, and project constraints
Navigate ethical considerations including bias, privacy, authorship and attribution, environmental impact, and responsible use
Articulate a personal framework for when and how to use AI in software development

Ethics Discussions

Ethics discussions will include the following topics:

AI training data and representation
Intellectual property with AI-generated code
Accessibility and inclusive design
Security ethics and disclosure
Privacy and data collection
Environmental impact of AI systems
Algorithmic bias
Job displacement and adaptation
Professional responsibility
Personal ethical frameworks

Please email Mike if you have ideas for additional topics that would be of interest!

The 15-113 Team

Mike Taylor

Taha Shakeel

Nazanin Ghazi

Alex Chen

Michelle Jiang

Topic List and Schedule

Week	Mon 12:30pm-1:50pm	Wed 12:30pm-1:50pm	Assignments	Cool Stuff
PHASE 1: TOOLS AND EXPLORATION (Weeks 1-4)
Week 1 AI Intro Bootcamp	Lecture 1 Slides Welcome & First Build • Course intro • Course ethos • Useful Tools	Lecture 1+2 Slides Building your Portfolio • Project 1 description • HTML crash course • "My First Webpage"	HW1: Intro Survey (due Fri 1/16 8pm) Start Project 1	Guide: Project 1 setup Guide: Github branches
Week 2 AI Intro Cont'd	No Class: M.L. King Jr. day	Lecture 3 Slides HW1 survey results Github Copilot Project 1 work time	Project 1 (due Sun 1/25 8pm)	Project 1 Example: Taha
Week 3 Core prompting strategies	Lecture 4 Slides Tetris 3 ways • Naive prompting • Plan-Adjust-Execute • Detailed prompts	Lecture 5 Slides More prompting strategies • Tetris debrief • More games	HW2: Crossy Road (due Sat 1/31 8pm)
Week 4 Core prompting strategies	Lecture 6 Slides Project 1 debrief • Discussion • Highlights Intro to APIs	Lecture 7 Slides HW2 debrief More API examples Build a chatbot in Python	HW3: Explore an API (due Sat 2/7 8pm)
PHASE 2: STRATEGIC MASTERY (Weeks 5-9)
PHASE 3: CAPSTONES AND BEST PRACTICES (Weeks 10-14)

Grading

Component	Weight	Notes
Homework (about 10)	20%	Mostly smaller, weekly projects. Grading based on evidence of effort, due Fridays
Participation	20%	Includes attendance, contribution to discussions and critiques/code reviews, and good collaboration
Big Projects (about 3)	40%	Weeks 10-14, ~12-15 hours total
Exams/Quizzes	20%	Infrequent quizzes + 2 exams. These will mostly ask you to reflect on tools and strategies we have discussed.

Letter Grades

A: [90-100], B: [80-90), C: [70-80), D: [60-70), R: [0-60)

Grading Philosophy: This course emphasizes effort, growth, and understanding over perfection. Grades reflect engagement with the material, demonstration of learning, and thoughtful use of AI tools. Code doesn't need to be production-perfect, but you must understand it and be able to explain your choices.

Projects

(Note that Projects 2 and 3 are especially tentative and subject to change)

Project 1: Personal Portfolio Website (Weeks 1-2)

Goal: Build a professional portfolio website to showcase your work—using AI to learn web development from scratch

Time: ~4 hours outside class, over two weeks

Context: Most students have never built a website before. This project teaches you to use AI as a learning tool to master new domains (HTML, CSS, JavaScript) while creating something immediately useful.

Requirements:

About Me section with bio, photo, interests, skills
Projects section with showcases (start with placeholders—you'll add real projects throughout the semester!)
Contact section with email, GitHub, LinkedIn, or contact form
Responsive design that works on desktop, tablet, and mobile
Professional appearance with clean, modern design
Optional: At least one interactive feature using JavaScript (animations, dark mode, etc.)
Deployed and live via GitHub Pages

Deliverables: Live URL, GitHub repo, informal reflection (don't use AI to write this part or do the thinking for you)

Important: Throughout the semester, you'll add each new project to your portfolio. By Week 14, you'll have a complete showcase of your work!

Project 2: Full-Stack Application (Weeks 6-8)

Goal: Build a complete, deployed web application

Time: ~8-10 hours

Requirements: User auth, database, frontend UI, deployed

Examples:

Task manager with sharing
Recipe collection with search
Fitness tracker with graphs
Study group organizer
Book club with reviews
Personal finance tracker

Deliverables: Live URL, GitHub repo, demo video (3 min), technical writeup, posted your portfolio website with screenshots and description

Project 3: Portfolio Capstone Project (Weeks 11-14)

Goal: Build something impressive that showcases your AI-augmented development skills!

Time: TBD

Requirements: Substantial complexity, professional quality, related to your interests or career path

Examples:

AI-powered study tool with intelligent features
Data visualization dashboard using real data
Multiplayer game with real-time networking
Mobile app solving a real problem
Browser extension with useful functionality
API service with comprehensive documentation
ML application with user interface
Community or campus-specific tool

Deliverables: In-class expo-style presentation, GitHub repo, interactive demo, technical writeup, posted your portfolio website with screenshots and description

Course Policies

AI Usage Policy

Philosophy: Transparency and learning over restriction

Required:

Document all significant AI usage in code comments and writeups
Explain AI-generated code in your own words
Modify and test AI suggestions, don't just copy-paste
Cite AI tools used

Encouraged:

Experiment with different tools and approaches
Share successful prompts and strategies with classmates
Use AI to learn new concepts and debug
Push AI to its limits to understand boundaries

Not Allowed:

Submitting AI code you can't explain reasonably well
Claiming AI-augmented work as wholly original
Any improper/missing attribution, or misrepresentation of submitted work and your process for creating it
Using AI on written exams or quizzes (except if explicitly permitted)

Verification: The instructor may ask you to explain any part of your submitted code, your process for creating it, or possible trade-offs and alternatives. Honesty and transparency always yields the best outcomes.

Collaboration Policy

Highly Encouraged:

Discuss ideas, strategies, and approaches
Debug together
Review each other's code
Share resources and prompts
Form study groups

Not Allowed:

Copying code from classmates, except where explicitly allowed
Submitting identical work to independent assignments
Doing someone else's project/assignment/assessment for them

General guidance: If you can teach them how to do it, great! If you're just giving them the answer, or if they can't recreate the work on their own without referencing notes or the original, you aren't collaborating. Ask if you aren't sure if something counts as good collaboration.

Academic Integrity

Core philosophy: You must understand and be able to explain all submitted code at a reasonably high level. More importantly, you must represent your work and your submissions with thorough transparency and proper attributions. When in doubt, ask if something is acceptable!

Violations include:

Submitting code you don't understand at a reasonable level
Copying from others (human or AI) without attribution
Misrepresenting your use of AI tools
Other violations of the collaboration / AI use policies above
Receiving or providing any unauthorized assistance or information on quizzes or exams
Using any prohibited tools or resources on quizzes or exams
Any other form of deliberate dishonesty or misrepresentation

Consequences: Penalties up to course failure, reported to university

Negligence or uncertainty is not an acceptable excuse. When in doubt: Ask! Better to ask than assume.

Late Policy

Homework / Mini-Projects:

On time: 100%
1-3 days late: 80%
4-7 days late: 50%
>7 days late: 0%
All late work must be submitted by the last day of class
In extenuating circumstances such as medical or family emergencies, email Mike or fill out the extension form listed below.

Major Projects:

Due dates are firm to allow for presentations and peer review
Extensions only for major documented emergencies (email Mike)
Extensions must be requested BEFORE deadline whenever possible

Exams and quizzes:

Extensions/excused absences for emergencies/illnesses must be requested in advance whenever possible.
Quizzes will typically be excused for emergencies. Make-up exams must be scheduled promptly.

Extensions and Excused Absences

If you need an extension or an excused absence due to extenuating circumstances such as medical or family emergencies or university-related conflicts, please fill out this form.

Please fill out the form before the deadline or class whenever possible. Extensions and excused absences will be less likely to be approved if not requested in a timely manner.
This form is just meant to streamline the process for common issues like brief illnesses, and it helps us keep track of requests. We'll generally be pretty flexible as long as requests are relatively rare and made promptly.
If your circumstance is particularly unusual or if you need additional support, always feel free to email Mike!

Tools and External Resources

Required Tools (All Free for Students)

Python 3.12+
Programming language. Download from python.org
Git & GitHub
Version control. Create account at github.com
GitHub Copilot
AI pair programmer. Free with GitHub Student Developer Pack
ChatGPT
Conversational AI assistant. Free tier available at chat.openai.com
VS Code
Code editor. Download from code.visualstudio.com
Python 3.12+
Programming language. Download from python.org

Recommended Tools (Free)

Claude - Good for complex explanations (free tier)
Cursor - Advanced IDE integration (free for students)
Replit - Online collaboration (free tier)
GitHub Codespaces - Cloud development environment

Course Materials

Weekly guides and prompt templates (on course website)
Code examples and starter files (on GitHub)
Ethics case studies (provided in class)
Tool comparison matrices (on course website)
Best practices checklists (on course website)

Office Hours and Support

Getting Help

Ed Discussion
Quick questions, sharing resources, peer support, course logistics
Instructor Office Hours in GHC 4126
For conceptual questions, project guidance, career advice
- Monday 2:00pm-3:30pm
- Thursday 12:00pm-1:30pm
TA Office Hours in the hall outside GHC 4126
For debugging help, tool assistance, assignment questions
- Wednesday 5:00pm-6:00pm
- Thursday 4:00pm-5:00pm
- Friday 2:00pm-4:00pm
In Class
Ask questions, work with peers, get immediate help.
Email
mdtaylor at andrew.cmu.edu
For private concerns, extension requests, accommodations

Accommodations

Students with documented disabilities should ensure that their memorandum of accommodations has been sent to 15-113 through the Office of Disability Resources portal. Soon after we receive your memorandum, we'll contact you with additional information on how you can use these accommodations. All tools and activities will be evaluated for accessibility, and alternatives will be provided as needed.

Additional notes

Original layout/some preliminary wording created with Claude Sonnet 4.5, Nov 2025. (In fact, we'll talk about this process in week 1!)
Some course policies adapted from CMU 15-112. F25