Making MGAIC
generative AI research with real-world impact
Advancing the frontiers of generative AI
MIT researchers are pushing the boundaries of model architecture, safety, and alignment to enable generative AI systems that are more capable, trustworthy, and open. These breakthroughs support discovery and creativity across science, health, education, and the arts.
Designing AI that elevates human work
MGAIC supports the development of tools that amplify human intelligence—enhancing productivity, creativity, and decision-making in real-world domains. The goal is not to replace people, but to create AI that collaborates, augments, and empowers.
Engineering for scalable, responsible deployment
Scaling generative AI requires addressing critical infrastructure challenges—from compute efficiency and power consumption to data integrity and system robustness. MIT’s cross-disciplinary expertise helps design AI systems that are both powerful and sustainable.
Expanding access through education
To ensure AI benefits are broadly shared, MGAIC promotes open-source tools, new models of learning, and global collaborations. With a focus on inclusion and opportunity, we are helping shape a future where everyone can participate in AI innovation.
“The remarkable progress in generative AI we’ve seen over the past year has been fueled by advances in fundamental science and engineering — areas where MIT excels.”
— Sally Kornbluth, President of MIT
Our founding members
Drive the strategic direction of the Generative AI Impact Consortium and fund project ideas from MIT’s research community.
Advisors to the Consortium
Student impact
The future of generative AI will be shaped by today’s students—and the MIT Generative AI Impact Consortium is committed to giving them a central role in that future.
Through research opportunities we support undergraduate and graduate students in tackling real-world challenges with generative AI. These opportunities empower students to collaborate with faculty and industry mentors, work on projects with societal significance, and gain hands-on experience at the forefront of AI research.
From developing open-source tools to exploring ethical deployment frameworks, students in the Consortium are not just learning about the future of AI—they’re building it. Their work spans disciplines, connects communities, and accelerates progress toward responsible, cross-sector solutions.
Funded projects
AI Models for Improving Human Agency
Team: Daron Acemoglu (Economics), Jacob Andreas (EECS), and Asuman Ozdaglar (EECS)
Area: AI for Society
This project will develop techniques for building AI systems that satisfy these conditions by establishing theoretical frameworks clarifying principles for effective human-AI collaboration; developing new learning algorithms optimizing for human compatibility that prioritize transparency, specificity, and predictability; and evaluating AI’s impact on human decision-making.
Empowering Underserved Students: AI-Driven Calculus Tutoring for Equitable Education
Team: Eric Klopfer (Comparative Media Studies/Writing) and Cynthia Breazeal (MIT Media Lab)
Area: AI for Education
Our project aims to address this gap by leveraging the success of PyTutor, an LLM tutoring platform developed at MIT RAISE. We propose to extend PyTutor to provide comprehensive multimodal and personalized tutoring support for advanced calculus courses in high schools with predominantly minority populations.