10 AI Project Ideas for High School Students
Whether you’re new to programming or ready to tackle an in-depth coding challenge, artificial intelligence (AI) automations offer high school students the opportunity to not only build new skills but also create unique pieces to add to their portfolio. For students interested in science, technology, engineering, and math (STEM) fields, AI projects offer the chance to gain hands-on experience with a variety of software such as Python and Java, along with exploring game design and development concepts, machine learning and automation processes. To help you get started, we’ve compiled a list of 10 independent AI project ideas you can try — ranked in order of difficulty and experience level, from the most beginner-friendly projects to more complex ones.
1. Rock, Paper, Scissors Game
In this classic project, students design the simple game of rock, paper, scissors where players compete against an AI opponent that relies on random number generation. This project can be created using conditional statements such as “if-else”, variable assignment and user input/output. This project is ideal for students looking for a beginner-friendly AI experience, as it introduces basic programming techniques.
Level of Coding: Beginner
Drawbacks: Predictability over time
Resources: Rock Paper Scissors Game Using AI Java
2. Image Classifier
Students develop a program that classifies images into distinct predetermined categories, such as animals, vegetables, tools, or buildings. To begin this project, students will need to gather a dataset of images divided into separate categories to help the program learn how to process the various objects or scenes within an image. This project is suited for students with basic programming knowledge and an interest in beginner-friendly AI automation.
Level of Coding: Beginner
Drawbacks: Limited predefined categories
Resources: TensorFlow Image Classification Tutorial
3. Pong Game
Students create a simple version of the retro arcade table tennis game, Pong, where players compete against an AI opponent. As players compete, the AI opponent adapts its behavior and difficulty level in response to the player’s ability using simple algorithms such as decision trees and reinforcement learning. This project is ideal for students looking for beginner-friendly experiences with programming and game development. Level of Coding: BeginnerDrawbacks: Repetitive play, difficulty designing a dynamic AI opponent
Resources: Beginner’s Python Tutorial: Pong
4. Simple Chatbot
Students create a simple AI-powered chatbot that can process, understand, and respond to basic user input and queries. Students can experiment with Natural Language Processing (NPL) through Python, which has a large variety of libraries to help simulate conversation. This project is ideal for students with an interest in language processing who are looking for a beginner-friendly experience with AI tools.
Level of Coding: Beginner
Drawbacks: Limited or basic predefined responses
Resources: Craft Your Own Python AI ChatBot
5. Email Spam Filter
Students create an email filter that uses machine learning algorithms such as Naive Bayes and deep learning models to analyze email messages, structure, and metadata. Students will provide examples of labeled datasets for both spam and non-spam emails to help the filter learn to distinguish the content between the two. This program is ideal for students with some experience with programming who are interested in learning more about machine learning algorithms.
Level of Coding: Beginner – Intermediate
Drawbacks: Imbalanced datasets, false positives
Resources: AI-based Spam Detection: In-depth Guide
6. Unbeatable Tic-Tac-Toe
Students create a simple — yet undefeatable — tic-tac-toe game in Python. In this AI-powered version of the classic children’s game, students integrate the graphical user interface (GUI), created using the Tkinter toolkit, with the game’s logic to build a fully functional computer game. This project is suited for students with foundational programming knowledge who are looking to build new skills.
Level of Coding: Beginner – Intermediate
Drawbacks: Unbeatable AI opponent, limited gameplay
Resources: Build a Tic-Tac-Toe Game Engine
7. Dungeon Master
Students develop a text-based fantasy adventure game where players go on quests and solve puzzles led by an AI-powered dungeon master. NPL techniques can be integrated to respond to players’ input to create a more immersive and responsive gameplay environment. This project is ideal for students with a solid foundation in programming who are interested in exploring NPL techniques and world-building game design.
Level of Coding: Intermediate
Drawbacks: Storyline and gameplay may become repetitive or incoherent
8. Virtual Pet
Students create a cute AI-powered virtual pet simulation game, where the pet — designed in Python using Tkinter as the GUI — can adapt to players’ actions and preferences over time. To help the pet respond to user input, students can use NLP or reinforcement learning techniques. This project is best suited for students with a basic understanding of game development and programming who want to explore game design, mechanics, user interface, and AI algorithms.
Level of Coding: Intermediate
Drawbacks: Maintaining simplicity and accessibility
Resources: Create Your Own Desktop Pet with Python
9. Chess
Students program an advanced AI-powered version of the classic board game, creating gameplay that is both challenging and engaging. The AI opponent relies on algorithms like minimax with alpha-beta pruning and/or machine learning techniques to move intelligently throughout the game. This project is ideal for students who want to work on an advanced, coding-heavy project to add to their college portfolio.
Level of Coding: Intermediate – Advanced
Drawbacks: Memory management, time-consuming development
10. Recommended Reading System
Students build a basic recommendation engine to suggest books and articles based on a user’s preferences and past behavior. Students can design the system to use content-based or collaborative filtering systems — or a combination of both — to analyze content from users’ past reviews and reading history. This project is also ideal for students seeking a complex, coding-heavy project for their college portfolio.
Level of Coding: Intermediate – Advanced
Drawbacks: Sparse data without database/API integrations
If you’re looking to build a project/research paper in the field of AI & ML, consider applying to Veritas AI!
Veritas AI is founded by Harvard graduate students. Through the programs, you get a chance to work 1-1 with mentors from universities like Harvard, Stanford, MIT, and more to create unique, personalized projects. In the past year, we had over 1000 students learn AI & ML with us. You can apply here!
If you’re interested in pursuing research in AI or related fields, you could also consider applying to one of the Lumiere Research Scholar Programs, selective online high school programs for students founded with researchers at Harvard and Oxford. Last year, we had over 4000 students apply for 500 spots in the program! You can find the application form here.
Also check out the Lumiere Research Inclusion Foundation, a non-profit research program for talented, low-income students. Last year, we had 150 students on full need-based financial aid!
Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a Ph.D. student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.