Joint Activity Design Heuristics for Enhancing Human-Machine Collaboration
This work addresses the challenge of improving coordination in human-machine teams, but it is incremental as it synthesizes existing literature into heuristics without introducing new methods or paradigms.
The paper tackles the problem of designing technologies for effective human-machine collaboration by synthesizing fourteen heuristics to support five key macrocognitive functions in teams, such as event detection and coordination, building on usability to enhance joint activity.
Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for effective coordination among agents engaged in the joint activity. This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players. Effective joint activity requires supporting, at minimum, five primary macrocognitive functions within teams: Event Detection, Sensemaking, Adaptability, Perspective-Shifting, and Coordination. Supporting these functions is equally as important as making technologies usable. We synthesized fourteen heuristics from relevant literature including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human-machine activity.