AI Scholars: Build Intelligent Systems For Social Good

• Use Python to explore and manipulate a real-world dataset

• Train a decision tree and conduct a bias audit

• Build and stress-test an image classifier

• Explore embeddings, semantic search, and prompt-engineering strategies

• Create a tool-calling AI agent and application workflow

• Build, deploy, and present an AI for Social Good app

By the end of the course, students will be able to:

• Train and evaluate introductory machine-learning models using Python

• Build an AI agent or application with defined tools and responsible constraints

• Develop, deploy, and present an evidence-tested AI project

Students leave with a live AI application, project presentation, and stronger skills in coding, evaluation, ethics, and deployment.

Building AI requires students to understand what happens between data and output. Training, testing, evaluation, and interpretation show why a model can appear accurate while still failing on important cases. Bias audits make the consequences of data choices visible.

Application design adds responsibility. Students define a useful problem, select tools, consider security and ethics, and test whether the system serves real people. These skills combine coding with research, communication, and judgment, foundations that matter across fields increasingly shaped by AI.

This course is for high school students who have completed 9th, 10th, 11th, or 12th grade and are interested in artificial intelligence, coding, data science, computer vision, language models, product design, or social-impact technology. It welcomes students who want to move from using AI tools to understanding and building them.

Participants should be ready for hands-on Python work, model testing, teamwork, ethical discussion, and iterative app development. Optional preparation materials support Python and AI basics, and mentors balance challenge with approachability. Persistence and willingness to debug are important.

Based on students surveyed in Summer Discovery’s 2026 program at the University of Michigan.

Programs on the University of Michigan campus give high-school students the experience of exploring an academic interest in Ann Arbor, a college town where campus and community life blend together. Tree-lined streets, downtown bookstores and cafés, the Nichols Arboretum, and trails near the Huron River create different ways to experience the setting beyond class.

Students learn alongside peers, navigate a large and active campus, and gain independence through everyday choices in a new environment. The experience helps students understand what university life can feel like while giving them space to deepen an interest without having every next step decided. For families, the program offers a meaningful combination of academic depth, community, personal growth, and clearer direction about what a student may want to study or pursue next.

Machine-learning engineers, data scientists, AI researchers, product managers, designers, and ethics or policy specialists build and evaluate intelligent systems. Their work spans healthcare, media, business, robotics, security, law, and public-interest applications.

Students practice that cycle through Python labs, classification, computer vision, NLP, agent building, app design, and an AI for Social Good capstone. Deploying a working application and explaining its limits offers a practical view of computer science, data science, research, product development, and responsible AI careers. It also shows how specialists collaborate across roles and communicate important decisions to colleagues, clients, or the public.