Grants

AI can accelerate scholarship in every discipline — and domain-specific scholarship is itself key for advancing AI. We work to catalyze that virtuous cycle.

Better AI makes better scholarship possible. Less obviously, the reverse is also true: the hardest problems in, for example, astronomy, medicine, economics, and the humanities expose the limits of current models and point toward what AI needs to become. HAI is built to close that loop.

Our work here has two dimensions. AI-driven discovery advances every discipline, where domain-specific scholarship can itself become a powerful vector for advancing AI — from decoding the physical universe from data, to understanding how ideas, language, and cultures evolve, to designing environments and therapies for human health. Foundations of AI pursue new architectures, algorithms, and evaluation approaches that go beyond simply adding silicon to scale models, including the search for shared principles underlying natural and artificial intelligence.

As AI reshapes work and society, what should people learn, and how should they learn it? We are building the rigorous evidence base to answer these questions.

Most conversations about AI in education begin and end with how AI transforms schooling. We start from a broader goal: supporting human flourishing in the age of AI. That reframing changes the research agenda.

We ask what knowledge and skills learners of all ages actually need as AI reshapes work, civic life, and the professions. We also focus on how to learn: developing AI-enabled learning systems that genuinely support students, teachers, communities, and institutions.

Answering these questions requires evidence gathered over time. We aim to build longitudinal research infrastructure — with Stanford’s own courses serving as a testbed — to track learner outcomes through their time at Stanford and well beyond graduation. This work extends from K–12 through university and lifelong learning.

AI is scaling at a breakneck pace while public trust in its governance remains unsettled. HAI offers a rigorous, empirically grounded alternative to the extremes dominating public conversation.

To shape AI for positive societal impact, you need to understand both how the systems work and how they transform incentives, institutions, and human lives. That requires technical and humanistic expertise working in dialogue — not going along parallel tracks. That integration is what Stanford HAI was built to provide.

Our research fuses technical AI expertise, design, and humanistic inquiry to study AI systems as they actually behave in the world and to develop new methods and tools to design them for the better. Then we translate that scholarship into clear, trusted guidance that leaders can depend upon to make decisions as AI continues to evolve.