About me

Distinguished Professor of Information Science, Syracuse University School of Information Studies

Kevin Crowston is a Distinguished Professor of Information Science at the Syracuse University School of Information Studies (aka the iSchool). He received his A.B. (1984) in Applied Mathematics (Computer Science) from Harvard University and a Ph.D. (1991) in Information Technologies from the Sloan School of Management, Massachusetts Institute of Technology.

Picture of Kevin

His research examines new ways of organizing made possible by the use of information technology. He approaches this issue in several ways: empirical studies of coordination-intensive processes in human organizations (especially virtual organization); theoretical characterizations of coordination problems and alternative methods for managing them; and design and empirical evaluation of systems to support people working together. Specific domains of interest include citizen science projects, data science teamwork and the future of journalism.

He is most recently a PI on an NSF HCC project: "Intelligent support for non-experts to navigate large information spaces" (21-06865) and PI on an NSF FW-HTF grant, "The Future of News Work: Human-Technology Collaboration of Journalistic Research and Narrative Discovery" (21-29047). With colleagues, he headed a Research Coordination Network to develop a socio-technical perspective on work in the age of intelligent machines.

He is co-editor-in-chief of the journal Information, Technology and People and was formerly editor-in-chief of ACM Transactions on Social Computing. He is an ACM Distinguished Speaker

Skill development and retention in the age of generative AI

The increased capability of modern artificial intelligence (AI) systems—especially generative AI—has raised widespread concerns about their impact‬‭. We define AI as “an emergent family of technologies that build on machine learning, computation and statistical techniques, as well as rely on large data sets to generate responses, classifications or dynamic predictions that resemble those of a knowledge worker”‬‭ [1]‬‭. In this project, we consider a long-standing concern about the impact of technology use, namely its effects on the skills of those using or affected by the system.