Thomas W. Malone

About

Thomas W. Malone is the Patrick J. McGovern Professor of Management and the Director of the MIT Center for Collective Intelligence at the Sloan School of Management. His work focuses on understanding and enhancing collective intelligence, which involves studying how groups, organizations, and networks can work together more effectively. As a leading figure in this field, Malone's research explores the ways in which technology and organizational design can improve decision-making, collaboration, and innovation within various social and technological systems.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Political Science
• Mathematics education
• Cognitive psychology
• Cognitive science
• Data science
• History
• Social psychology

Selected publications

• Making Learning Fun : A Taxonomy of Intrinsic Motivations for Learning

  2021 · 910 citations

  1st authorCorresponding

  Over the past 2 decades, great strides have been made in analyzing the cognitive processes involved in learning and instruction. During the same period, however, attention to motivational issues has been minimal. It is now time to redress this imbalance. As Bruner (1966) has put the case: The will to learn is an intrinsic motive, one that finds both its source and its reward in its own exercise. The will to learn becomes a ‘problem’ only under specialized circumstances like those of a school, wh…

  PublisherDOI

• When combinations of humans and AI are useful: A systematic review and meta-analysis

  Nature Human Behaviour · 2024-10-28 · 306 citations

  reviewOpen accessSenior author

  Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human-AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdiscip…

  PublisherOA PDFDOI

• Intrinsic Motivation and Instructional Effectiveness in Computer-Based Education

  2021 · 190 citations

  Senior authorCorresponding

  Our goal in this chapter is to examine the relationship between intrinsic motivation and instructional effectiveness, in the context of the study of computer-based educational activities for children. In so doing, our hope is to illustrate the value of using computer-based learning as a laboratory for reviving classic issues in educational and social psychology and for examining those issues in a manner that highlights both their considerable theoretical significance and their immediate social i…

  PublisherDOI

• Quantifying collective intelligence in human groups

  Proceedings of the National Academy of Sciences · 2021 · 163 citations

  Collective intelligence (CI) is critical to solving many scientific, business, and other problems, but groups often fail to achieve it. Here, we analyze data on group performance from 22 studies, including 5,279 individuals in 1,356 groups. Our results support the conclusion that a robust CI factor characterizes a group's ability to work together across a diverse set of tasks. We further show that CI is predicted by the proportion of women in the group, mediated by average social perceptiveness…

  PublisherOA PDFDOI

• DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration

  2023-10-13 · 72 citations

  articleOpen accessSenior author

  Designers often struggle to sufficiently explore large design spaces, which can lead to design fixation and suboptimal outcomes. Here we introduce DesignAID, a generative AI tool that supports broader design space exploration by first using large language models to produce a range of diverse ideas expressed in words, and then using image generation software to create images from these words. This innovative combination of AI-based capabilities allows human-computer pairs to rapidly create a dive…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Measuring Collective Intelligence

  NSF · $538k · 2010–2014

• VOSS: Collaborative Research: Is Larger Smarter? Investigating the Effect of Group Size on Collective Intelligence

  NSF · $328k · 2013–2017

• CyberSEES:Type 2: Collaborative Research: Combining Experts and Crowds to Address Challenging Societal Problems

  NSF · $599k · 2015–2018

Frequent coauthors

• Kevin Crowston

  24 shared

• Robert Laubacher

  Massachusetts Institute of Technology

  24 shared

• Jintae Lee

  Yeungnam University

  14 shared

• Anita Williams Woolley

  Carnegie Mellon University

  12 shared

• Kum‐Yew Lai

  9 shared

• George Herman

  Eaton (United States)

  9 shared

• Kenneth R. Grant

  8 shared

• Robert I. Benjamin

  7 shared

Labs

• MIT Center for Collective IntelligencePI

Awards & honors

• Roosevelt “Rosey” Thompson Award from the U.S. Presidential…

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