TY - JOUR
T1 - Design patterns for human-AI co-learning
T2 - A wizard-of-Oz evaluation in an urban-search-and-rescue task
AU - Schoonderwoerd, Tjeerd A.J.
AU - Zoelen, Emma M.van
AU - Bosch, Karel van den
AU - Neerincx, Mark A.
PY - 2022
Y1 - 2022
N2 - The rapid advancement of technology empowered by artificial intelligence is believed to intensify the collaboration between humans and AI as team partners. Successful collaboration requires partners to learn about each other and about the task. This human-AI co-learning can be achieved by presenting situations that enable partners to share knowledge and experiences. In this paper we describe the development and implementation of a task context and procedures for studying co-learning. More specifically, we designed specific sequences of interactions that aim to initiate and facilitate the co-learning process. The effects of these interventions on learning were evaluated in an experiment, using a simplified virtual urban-search-and-rescue task for a human-robot team. The human participants performed a victim rescue- and evacuation mission in collaboration with a wizard-of-Oz (i.e., a confederate of the experimenter who executed the robot-behavior consistent with an ontology-based AI-model). The designed interaction sequences, formulated as Learning Design Patterns (LDPs), were intended to bring about co-learning. Results show that LDPs support the humans understanding and awareness of their robot partner and of the teamwork. No effects were found on collaboration fluency, nor on team performance. Results are used to discuss the importance of co-learning, the challenges of designing human-AI team tasks for research into this phenomenon, and the conditions under which co-learning is likely to be successful. The study contributes to our understanding of how humans learn with and from AI-partners, and our propositions for designing intentional learning (LDPs) provide directions for applications in future human-AI teams.
AB - The rapid advancement of technology empowered by artificial intelligence is believed to intensify the collaboration between humans and AI as team partners. Successful collaboration requires partners to learn about each other and about the task. This human-AI co-learning can be achieved by presenting situations that enable partners to share knowledge and experiences. In this paper we describe the development and implementation of a task context and procedures for studying co-learning. More specifically, we designed specific sequences of interactions that aim to initiate and facilitate the co-learning process. The effects of these interventions on learning were evaluated in an experiment, using a simplified virtual urban-search-and-rescue task for a human-robot team. The human participants performed a victim rescue- and evacuation mission in collaboration with a wizard-of-Oz (i.e., a confederate of the experimenter who executed the robot-behavior consistent with an ontology-based AI-model). The designed interaction sequences, formulated as Learning Design Patterns (LDPs), were intended to bring about co-learning. Results show that LDPs support the humans understanding and awareness of their robot partner and of the teamwork. No effects were found on collaboration fluency, nor on team performance. Results are used to discuss the importance of co-learning, the challenges of designing human-AI team tasks for research into this phenomenon, and the conditions under which co-learning is likely to be successful. The study contributes to our understanding of how humans learn with and from AI-partners, and our propositions for designing intentional learning (LDPs) provide directions for applications in future human-AI teams.
KW - Design patterns
KW - Human-AI co-learning
KW - Human-AI collaboration
KW - Learning design patterns
KW - Urban-search-and-rescue
KW - Wizard-of-Oz study
UR - http://www.scopus.com/inward/record.url?scp=85128204232&partnerID=8YFLogxK
U2 - 10.1016/j.ijhcs.2022.102831
DO - 10.1016/j.ijhcs.2022.102831
M3 - Article
AN - SCOPUS:85128204232
VL - 164
JO - International Journal of Human-Computer Studies
JF - International Journal of Human-Computer Studies
SN - 1071-5819
M1 - 102831
ER -