September 25th, 2026 12PM EST: Humans, AI, Tools, and Teammates: A Facilitated Conversation
Abstract:
The Organizational Teams and Technology Research Society (OTTRS) invites you to join us for a moderated discussion on AI and teams between Dr. Ben Shneiderman and Dr. Chirag Shah, facilitated by Dr. Susan Winter. The discussion will cover topics such as whether AI can be a teammate and ways for AI to support teams. The two experts will share how they get interested in this field and future directions for AI-and-team research and practical applications. We will also give time for the audience to ask relevant questions near the end of the discussion.

Susan Winter: Associate Dean for Research, University of Maryland College of Information
Bio:
Susan Winter studies the organization of work and the co-evolution of technology and work practices with a recent focus on ethical issues, emerging information technologies, the social and organizational challenges of data reuse, and collaboration among information workers and scientists. Her work has been supported by the U.S. National Science Foundation and by the Institute of Museum and Library Services and resulted in over 80 publications. She previously served as a Science Advisor in the Social Behavioral and Economic Sciences Directorate, a Program Director, and Acting Deputy Director of the Office of Cyberinfrastructure at the National Science Foundation supporting distributed, interdisciplinary scientific collaboration for complex data-driven and computational science. She received her PhD from the University of Arizona, her MA from the Claremont Graduate University, and her BA from the University of California, Berkeley.

Ben Shneiderman: Distinguished Unviersity Professor Emeritus, University of Maryland
Bio:
BEN SHNEIDERMAN (http://www.cs.umd.edu/~ben) is an Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://hcil.umd.edu), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. He is a Fellow of the AAAS, ACM, IEEE, NAI, and the Visualization Academy and a Member of the U.S. National Academy of Engineering. He has received six honorary doctorates in recognition of his pioneering contributions to human-computer interaction and information visualization.
Ben is the lead author of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016). He wrote The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, 2016) to describe how researchers can increase their impact. His book, Human-Centered AI (Oxford, 2022), won the Association of American Publishers award for Computer and Information Systems,
References:

Chirag Shah: Professor, University of Washington
Bio:
Chirag Shah is a Professor of Information and Computer Science at the University of Washington, where he serves as Founding Director of the InfoSeeking Lab and Founding Co-Director of RAISE (Center for Responsibility in AI Systems & Experiences). His research focuses on AI agents, responsible AI, human-centered information seeking, and agentic information access systems, with a recurring intellectual thread around the “Delegation Paradox” — the tension between human agency and task accomplishment through AI delegation. He has authored nearly 200 peer-reviewed articles and nine books, including the recent Agent Nation (Apress/Springer). He maintains active industry collaborations with Amazon, Microsoft Research, and others, and holds Distinguished Member status in both ACM and ASIS&T. He advises PhD students across proactive conversational search, agentic AI, and LLM evaluation, and contributes regularly to public discourse on responsible AI through press, talks, and editorials.
References:
May 15th, 2026: Accurate, Up-To-Date, and Shared Knowledge? A Human-Robot Team Cognition Framework
Speaker: Susan J. Simkins, Professor, Industrial-Organizational Psychology; Director of Team Science for the Clinical and Translational Science Institute (CTSI); Director, Teams, Cognition, and Time (TCaT) Lab
Abstract:
While robots can enhance efficiency and safety, they may also increase cognitive and coordination demands if not properly integrated. In response to the call for theoretical models in human autonomy teaming, Dr. Simkins and colleagues developed a conceptual framework that integrates multiple forms of team knowledge to inform how robots should be onboarded, enabling team members to develop an accurate, up-to-date, and shared understanding of robot capabilities. Specifically, the framework identifies three core properties of team cognition: accuracy (holding correct knowledge about robot capabilities and limitations), updating (the ability to adapt knowledge as conditions and robot behaviors change), and sharedness (alignment among team members regarding the robot’s role in taskwork, teamwork, and timework). These properties reflect distributed, dynamic, and overlapping forms of team knowledge, respectively, and are shaped by contextual factors. By integrating fragmented concepts from prior research, this model provides a cohesive foundation for examining how team cognition influences coordination and performance in human–robot teams and offers direction for future research in this rapidly evolving domain.
Bio:
Susan Simkins (formerly Mohammed) is a professor of Industrial-Organizational psychology at The Pennsylvania State University. She leads the Teams, Cognition, and Time lab, where her work is funded by the National Science Foundation, the National Institutes of Health, and the Office for Naval Research, among others. Her research focuses on team cognition, team composition/diversity, and the role of time in team and leadership research. Her most recent work applies these research topics to human-robot teaming. Dr. Simkins is a fellow of the Association of Psychological Science and the Society for Industrial and Organizational Psychology.
April 17th, 2026: “You have to give a shit”: Information brokering as sociomaterial infrastructuring
Speaker: Connie Siebold, PhD Candidate and Instructor, College of Information, University of Maryland College Park
Abstract:
This work explores the process of information transfer through the lens of infrastructure. Focusing on brokering as a behavior that includes the facilitation of connections and knowledge transfer across organizational and disciplinary boundaries, it examines a wide array of actors across contexts to understand brokering in its less visible forms. Via longitudinal interviews and artifact capture, the work highlights microdynamics and contextually rooted behavioral strategies that brokers use to connect communities. These broker experiences are applied to current models of brokering in an effort to contextualize high-level understandings of practice and flesh out the invisible labor involved in brokering. This contributes insights into the mechanisms, tools, and relationship formation involved in brokering, taking a practice view of the phenomenon, and serves as a bestiary of brokering behavior for managers seeking to reward the valuable but obscured contributions of their employees.
Bio:
Connie is a PhD candidate in the INFO College at UMD. Her primary dissertation research focuses on interpersonal connectivity and how information flows through people and systems – the study of ‘knowing a guy who knows a guy’. She also teaches in the INFO college and moonlights as a researcher or assistant on projects ranging from disability and accessibility to sociolinguistic identity and English language education. As a former librarian and bartender, she is happy to answer strange questions at strange hours and probably ‘knows a guy’ if you need one.
February 20th, 2026: “The Coevolution of Computational and Experimental Methods in Human-AI Teams“
Speaker: Dr. Neal Outland, Assistant Professor of Industrial-Organizational Psychology, University of Georgia ; AI Faculty Fellow of the Institute for Artificial Intelligence
Abstract:
The study of human-AI teams, rife with unforeseeable conditions and dynamics, requires dynamic interplay between theoretical foundations, computational modeling, and experimental validation. Neal Outland (University of Georgia) explores how this coevolution advances HAT research. Part one discusses a systematic review of trust theories across disciplines, revealing a fragmented landscape of psychological, computational, organizational, and engineering perspectives. He demonstrates how computational modeling and experimental work have been used collectively to translate theory into practice. Part two presents a glance into the future: bidirectional trust models emerging from iterative simulation-experiment cycles, and identity dynamics frameworks evolving through computational exploration and empirical validation. These approaches show how computational methods and experimental data may mutually inform each other, capturing emergent phenomena that single methodologies miss. The talk concludes with future directions where theory, computation, and experimentation form integrated discovery cycles developing adaptive AI systems through real-time modeling, incorporating individual differences into computational frameworks, and creating testbeds that simultaneously validate and inspire theoretical insights.
Bio:
Neal Outland is Assistant Professor of Industrial-Organizational Psychology at the University of Georgia and AI Faculty Fellow of the Institute for Artificial Intelligence. His research bridges computational and experimental methods to understand human-AI team dynamics, trust calibration, and identity processes in organizations. He has published systematic reviews of trust theories across disciplines, developed bidirectional computational models of human-robot interaction, and designed innovative experimental paradigms including 3D virtual testbeds. His work on team composition, social network approaches, and individual differences in human-AI collaboration appears in American Psychologist, Organizational Psychology Review, Current Opinion in Psychology, and IEEE conference proceedings. Recent projects supported by DEVCOM Analysis Center and other agencies examine how personality and attitudes shape trust evolution in human-autonomy teams. He focuses on integrative approaches where computational and experimental methods coevolve to advance both theoretical understanding and practical applications for human-AI teaming.