Tackling team science: New CCTS research sheds light on AI and collaboration
LEXINGTON, Ky. (Oct. 1, 2026) — Team science is essential to tackling complex biomedical problems, but it comes with unique challenges. The biostatistics, research analytics, implementation science, data science and informatics, guidance and epidemiology (BRIDGE) team of the UK Center for Clinical and Translational Science addresses two such difficulties—evaluating success in interdisciplinary teams, and using AI in collaborative research—in papers recently published in Frontiers in Psychology.
The first paper, “Rethinking team science metrics through Collaborative Guideposts,” proposes a framework to evaluate team science ventures, acknowledging that traditional metrics like publications and grants often fail to capture important contributions in areas such as translation, community engagement and workforce development. The proposed framework aligns existing metrics with a team’s purpose, structure and timeline rather than introducing new measures. By incorporating factors like time horizon, unit of assessment and desired outcomes, the Collaborative Guideposts approach helps ensure that meaningful contributions are recognized, including those that support long-term impact but do not produce immediate, traditional outputs.
The journal article provides two contrasting team science examples that illustrate how different team models require different definitions of success. The cases emphasize the need for flexible evaluation approaches that can be applied across diverse research settings and goals, such as mentorship, methodological collaboration and community partnerships. From these examples, the authors propose four “Collaborative Guideposts”—time perspective, unit of assessment, desired outcomes and sustainability—that provide a structured yet flexible way to capture team success.
“Overall, this work provides an actionable framework that supports more meaningful evaluation, better alignment between team goals and institutional expectations, and improved sustainability of collaborative research efforts,” said Emily Slade, Ph.D., associate professor of biostatistics and director of CCTS BRIDGE.
The second paper, “Artificial intelligence in biomedical team science: perceptions, practices, and training needs,” examines researchers’ experience with and attitudes toward AI use in collaborative research environments. The study is one of the first to assess how biomedical researchers perceive and integrate AI tools in team science settings.
“While the use of artificial intelligence in biomedical research is rapidly expanding, most existing literature has emphasized technical performance and applications rather than the social and collaborative dimensions of AI integration within research teams. Few studies have examined how AI is understood, discussed, or adopted in multidisciplinary research settings,” the authors write.
The paper’s findings, based on a survey of 178 investigators at the University of Kentucky, highlight gaps between perceived benefits of and current practices with AI. The authors suggest a need for evidence-based training that supports responsible and effective AI use on collaborative research teams.
Slade says that studying the “science of team science” is crucial for empowering researchers to effectively collaborate.
"The rigor of biomedical research depends not only on the methods we use, but on how effectively we work together to apply them. Biostatisticians and other quantitative scientists have an important leadership role in those teams, making team science an essential part of doing rigorous quantitative research."
The project described was supported by the NIH National Center for Advancing Translational Sciences through grant number UL1TR001998. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Media Contact: Mallory Profeta, mallory.profeta@uky.edu
