Why AI x Science?

We define AI as the quest to build machines that can reason, learn, and act intelligently. Machine learning is a component of AI where machines learn patterns from data to make predictions or to generate content. In the activities in this website, AI and ML are presented as part of the modern data science toolbox that scientists use, and that youth need to learn about as AI proliferates and becomes part of their everyday lives.

What was this project's origin?

For over a decade, the Science Research Mentoring Program has provided paid research opportunities for high school students to conduct authentic research alongside scientists at the American Museum of Natural History (AMNH). In the late 2010s, we began to notice some of our scientist-mentors teaching themselves (and their high school mentees) new AI techniques like artificial neural networks. That led to an idea. Can we help high school students get a jump on this new technology and teach high school students about scientific inquiry and the fundamentals of AI simultaneously? And can we do so in a way that is equitable and supports a diversity of learners? To do that, we needed expertise in AI education, scientific research, and justice-based learning. The Machine Learning (ML) Science Institute was born in partnership with Massachusetts Institute of Technology Scheller Teacher Education Program, and drawing on the evidence-based framework created by the Youth Equity and STEM (YESTEM) project, work from across AMNH, and an advisory board of youth and young professionals.

What is the ML institute all about?

The Machine Learning Institute is grounded in the belief that students can learn not only the mechanics of science and machine learning but also critically examine when and why to use these tools through a justice-based perspective -thinking both about scientific and societal implications of this new technology. The institute encourages learners to understand the power and limitations of machine learning, exploring both its scientific potential and its ethical implications. By combining scientific inquiry with considerations of equity and justice, students gain a holistic view of how ML can be responsibly integrated into research, innovation, and real-world applications.

Why High School Students?

High school is a launchpad when young people start shaping their futures and deciding how they’ll engage with the big ideas shaping our world. Introducing AI - in the context of doing scientific research - builds literacy tied to authentic, real-world challenges, and exposures youth to career opportunities that shape their college choices

Did it work?

We set out to develop an experience where students could learn about AI through scientific inquiry, and they did! (Moore et al. 2026). Students’ knowledge of basic machine learning concepts went up after the institute, and this increase was most pronounced for students of color. (Rabinowitz et al. 2025). We have some evidence that it persisted across 9 months (Weckel et al., in prep)! Interviews with students also revealed that students felt more confident in determining whether using AI to solve a problem was technically necessary and ethically responsible. They could articulate the steps of the machine learning pipeline, including differences in algorithms. They left our Institute aware of AI’s increasing role in their personal lives, and more confident to include AI in future pursuits. Notably, post-Institute students gravitated toward what we term informed neutrality, a stance toward AI characterized by curiosity, caution, and ethical awareness.

• Meet the Team