Adapt and Overcome: Perceptions of Adaptive Autonomous Agents for Human-AI Teaming

Using a mixed methods approach, we recruited 103 participants to complete a factorial survey containing ten contextual vignettes focused on an AI teammate’s level of autonomy in incident response contexts, and from these participants we conducted twenty-two follow-on qualitative interviews that further explored how the participants felt an AI agent’s adaptive capabilities would affect team performance and cohesiveness. Our results showed that work cycles can be used to assign autonomy levels to adaptive AI agents based upon the degree of formal processes and predictability of the team’s tasks during the cycle, and that dynamic, human-like adaptation methods are vital to effective human-AI teams. This research provides significant contributions to the HCI community by proposing design recommendations for the development of adaptive autonomous teammates that both enhance Human-AI teams’ productivity and promote positive team dynamics.