The third Stanford AI+Education Summit brought together cross-sector experts to address the strategic implementation of AI in educational settings, revealing how forward-thinking institutions are developing governance frameworks and implementation roadmaps to maximize learning outcomes while addressing privacy concerns.
As artificial intelligence transforms educational capabilities—from summarising text and debugging code to creating images and recording conference proceedings—education leaders are facing unprecedented opportunities and challenges in implementation.
The summit highlighted organisations already demonstrating measurable progress in AI integration. New York City Public Schools, the nation's largest district, has implemented professional development for 10,000 staff members and established a K-12 AI Policy Lab with strategic partnerships across research institutions, philanthropic organisations, and technology providers.
Peninsula School District in Washington has taken a similarly structured approach by establishing an AI action research team and developing collaborative relationships with other districts and university partners. These implementations demonstrate how educational institutions are moving beyond exploratory phases to systematic deployment strategies.
The summit underscored a critical insight for enterprise implementation: only 26 states have issued official guidance on AI use in education, creating significant operational challenges for educators and administrators. Without clear governance frameworks, many schools default to prohibiting AI tools entirely—a strategy that introduces serious equity concerns as students with home access gain advantages over those without.
Research presented at the summit demonstrated how AI implementations are addressing specific educational challenges:
Victor Lee, associate professor of education at Stanford, revealed findings on current AI usage patterns among high school students, including grammar checking and collaborative work facilitation. His research identified three primary professional development needs among teachers: understanding how to use AI in teaching, how to teach about AI, and how AI functions. This research coincides with emerging legislation, such as California's requirement to incorporate AI literacy into curriculum.
Michael Frank, Benjamin Scott Crocker professor of human biology at Stanford, demonstrated how language models can advance understanding of child development through the BabyView project, which uses head cameras to collect data on children's language acquisition processes. This research is expanding globally through the Learning Variability Network Exchange (LEVANTE), creating culturally adaptable datasets that enhance AI model performance across diverse populations.
Emma Brunskill, associate professor of computer science, showcased research on using AI to accelerate education innovation cycles by simulating and optimising interventions before human evaluation—potentially reducing research timelines from decades to years.
The summit revealed a critical strategic consideration for education-focused AI implementations: the distinction between automation (replacing human capabilities) and augmentation (enhancing human abilities). Organisations that prioritise augmentation approaches report stronger stakeholder buy-in and more sustainable implementation processes.
Education leaders across public and private sectors emphasized that successful enterprise AI deployments must integrate privacy protections, bias mitigation strategies, and multi-stakeholder design processes from inception. Organisations collaborating with researchers, technologists, teachers, students, and policymakers throughout implementation show measurably stronger outcomes than those pursuing technology-first approaches.