Enhancing Productivity with AI and Human-Computer Interaction

MIT’s Interdisciplinary Approach

The MIT Stephen A. Schwarzman College of Computing has funded seven interdisciplinary projects through seed grants that explore artificial intelligence (AI) and human-computer interaction in modern workspaces. The projects, funded by Andrew W. Houston ’05 and Dropbox Inc., aim to improve management and productivity by combining computing, social sciences, and management research. The results of these projects have the potential to kickstart larger efforts in this rapidly evolving area and develop a community centered around AI-augmented management.

Selected Projects and Research Leads

LLMex: Implementing Vannevar Bush’s Vision of the Memex Using Large Language Models

Led by Patti Maes from the Media Lab and David Karger from the Department of Electrical Engineering and Computer Science (EECS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL), this project aims to create an AI-based system that helps individuals track vast amounts of information by recording their work actions, supporting retrieval based on metadata, and suggesting relevant personalized information based on the user’s current focus and context.

Using AI Agents to Simulate Social Scenarios

John Horton from the MIT Sloan School of Management and Jacob Andreas from EECS and CSAIL lead this project, which focuses on simulating policies, organizational arrangements, and communication tools with AI agents before implementation. Utilizing modern large language models (LLMs) as a computational model of humans can make social simulations more realistic and predictive.

AI and Human Expertise in Various Settings

Human Expertise in the Age of AI: Can We Have Our Cake and Eat It Too?

Manish Raghavan from MIT Sloan and EECS and Devavrat Shah from EECS and the Laboratory for Information and Decision Systems lead this project. The project argues that AI and algorithmic decision aids will complement human decision-making, rather than replace human professionals across various settings.

Implementing Generative AI in U.S. Hospitals

This project, led by Julie Shah from the Department of Aeronautics and Astronautics and CSAIL, Retsef Levi from MIT Sloan, Kate Kellogg from MIT Sloan, and Ben Armstrong from the Industrial Performance Center, aims to develop a holistic framework to study how generative AI technologies can increase productivity in healthcare organizations and improve job quality for healthcare workers.

Democratizing Programming and Supporting Users

Generative AI Augmented Software Tools to Democratize Programming

Harold Abelson from EECS and CSAIL, Cynthia Breazeal from the Media Lab, and Eric Klopfer from Comparative Media Studies/Writing lead this project. They strive to create a software tool that eliminates the need for learners to deal with code when creating applications, transforming computing education for those without prior technical training.

AI Augmented Onboarding and Support

Tim Kraska from EECS and CSAIL and Christoph Paus from the Department of Physics lead this project. They propose the development of new LLM-powered onboarding and support systems to improve the support team’s efficiency and user experience while mitigating the learning curve associated with resource utilization.

Analyzing AI’s Impact on Skill Acquisition and Productivity

Acquiring Expertise and Societal Productivity in a World of Artificial Intelligence

David Atkin and Martin Beraja from the Department of Economics and Danielle Li from MIT Sloan lead this project. They aim to understand how AI technologies may impact skill acquisition and productivity and explore complementary policy interventions to maximize societal gains from these technologies.