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Collaborative Research: FW-HTF-RM: Intelligent Facilitation for Teams of the Future via Longitudinal Sensing in Context

合作研究：FW-HTF-RM：通过上下文中的纵向感知为未来团队提供智能协助

基本信息

批准号：
1928718
负责人：
Gloria Mark
金额：
$ 33.94万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928718&HistoricalAwards=false
关键词：
Collaborative Research FW HTF RM

项目摘要

In the information workplace of the future, teamwork will become increasingly critical and teamwork itself will be redefined. Teams will need to develop better skills in handling complex problems as routine work will be increasingly delegated to artificial intelligence (AI) technologies such as personal digital assistants. Teams will need to rapidly adapt to fluid membership and changing work structures with the growing gig economy, and as new workers enter the workforce bringing new cultural practices. Individuals will need to be able to perform effectively in heterogeneous teams as the workforce becomes more diverse and as globalization increases. The future of teamwork will require integration of technological advances to facilitate team performance, yet we are largely relying on tools and techniques from the 20th century for team facilitation. This project will develop and validate an intelligent (AI-based) team facilitator for information work utilizing sensing and dynamic intervention to promote better team coordination, higher performance, and ultimately lower worker burnout. The intelligent team facilitator will serve as a blueprint for a broad set of domains beyond information work, including medical care teams, control room settings, crisis management, and manufacturing, where team skills will be needed for interacting with AI, robots, and new technologies. The facilitator can also be used for training underrepresented groups to succeed in the workforce, a national priority. The present project utilizes sensor technologies for tracking team behavior in information workplaces in addition to traditional methods of studying teams using observations and self- reports. Longitudinal precision tracking of teams in situ with a suite of sensors can provide objective measures, can scale, and will enable a deep understanding of how teams respond to changing contexts, how teams form and integrate new members, and how they develop rhythms of teamwork. This project examines team diversity broadly, considering demographics, attitudes, circadian rhythms and personal responsibilities. The first aim of this project is to develop models of critical team states and processes (e.g., team cohesion, team coordination, team mood/affect), based on unobtrusive, continual, longitudinal sensing of physiology, behavior, and communication in a real-world context along with measures of individual differences to understand factors that lead to team effectiveness. This project will use risk mitigation strategies to safeguard privacy and security of data. The second aim of this project is to use those insights to develop an intelligent (AI-based) team facilitator. Performance of teams who use the intelligent team facilitator will be experimentally compared against matched controls in a longitudinal in situ study. The results will contribute to a new understanding on how 21st century teams can manage complexity, how team heterogeneity can lead to team effectiveness, and will identify successful strategies for team adaptability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在未来的信息工作场所，团队合作将变得越来越重要，团队合作本身将被重新定义。团队将需要培养更好的处理复杂问题的技能，因为日常工作将越来越多地委托给个人数字助理等人工智能(AI)技术。随着零工经济的发展，以及新员工进入劳动力大军带来新的文化实践，团队需要迅速适应不断变化的成员和不断变化的工作结构。随着劳动力变得更加多样化和全球化程度的提高，个人将需要能够在不同的团队中有效地发挥作用。团队合作的未来将需要整合技术进步来促进团队绩效，但我们在很大程度上依赖于20世纪的工具和技术来促进团队。该项目将开发和验证用于信息工作的智能(基于人工智能的)团队促进器，利用传感和动态干预来促进更好的团队协调、更高的绩效，并最终降低员工的职业倦怠。智能团队协调人将作为信息工作以外的广泛领域的蓝图，包括医疗团队、控制室设置、危机管理和制造，在这些领域，与人工智能、机器人和新技术的互动将需要团队技能。调解人还可用于培训任职人数不足的群体，使其在劳动力中取得成功，这是国家的优先事项。除了使用观察和自我报告研究团队的传统方法外，本项目还利用传感器技术来跟踪信息工作场所的团队行为。使用一套传感器对团队进行现场的纵向精确跟踪可以提供客观的测量，可以扩展，并将使团队能够深入了解团队如何应对不断变化的环境，团队如何组建和整合新成员，以及他们如何发展团队合作节奏。这个项目广泛地考察了团队的多样性，考虑了人口统计、态度、昼夜节律和个人责任。这个项目的第一个目标是开发关键团队状态和过程的模型(例如，团队凝聚力、团队协调、团队情绪/情感)，基于真实世界环境中对生理、行为和沟通的不显眼的、连续的、纵向的感知，以及对个体差异的测量，以了解导致团队有效性的因素。该项目将使用风险缓解策略来保护数据的隐私和安全。这个项目的第二个目标是利用这些洞察力来开发一个智能(基于人工智能的)团队协调人。在一项纵向的现场研究中，将对使用智能团队促进器的团队的表现与匹配的对照组进行实验比较。这一结果将有助于对21世纪团队如何管理复杂性、团队异质性如何导致团队有效性的新理解，并将确定团队适应性的成功战略。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Supervised Compression for Resource- constrained Edge Computing Systems

资源受限边缘计算系统的监督压缩

DOI：
10.1109/wacv51458.2022.00100
发表时间：
2022
期刊：
IEEE Winter Conference on Applications of Computer Vision (IEEE WACV
影响因子：
0
作者：
Matsubara, Y;Yang, R.;Mandt, S;Levorato, M.
通讯作者：
Levorato, M.

Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions

告诉我你自己：使用人工智能驱动的聊天机器人进行带有开放式问题的对话式调查

DOI：
10.1145/3381804
发表时间：
2020
期刊：
ACM Transactions on Computer-Human Interaction
影响因子：
3.7
作者：
Xiao, Ziang;Zhou, Michelle X.;Liao, Q. Vera;Mark, Gloria;Chi, Changyan;Chen, Wenxi;Yang, Huahai
通讯作者：
Yang, Huahai

Neural Transformation Learning for Deep Anomaly Detection Beyond Images