Adaptable and Hybrid Automation for Human–AI Task Allocation: Application to Call Center Supervision

In complex environments, Operators need to manage continuous, real-time information flows while handling unexpected situations within a limited timeframe. This can lead to high cognitive load, stress, fatigue, etc. To prevent such situations, Artificial Intelligence (AI) systems are increasingly being considered. Their role is no limited to assistance, but extends to performing some or all of the tasks initially carried out by the operator. However, inappropriate allocation of tasks between humans and machines can exclude the operator from the loop or reduce their vigilance. This paper proposes the design and implementation of three strategies for the dynamic reallocation of tasks between a human and an AI, considering factors related to the operator (cognitive load, stress) and their activity (activity modeling). An evaluation is conducted to compare three strategies. The first two are hybrid strategies, in which both the operator and the AI can modify task allocation. The first hybrid strategy is based on self-assessment, while the second is based on activity modeling. The third strategy is an adaptable strategy, in which only the operator can change task allocation. The use case is an emergency call center simulation implemented on InteractiveAI Preliminary findings from our user exploratory study suggest that participants tended to better accept adaptable automation, while also exhibiting a higher error distribution compared to hybrid automation strategies. No significant differences were observed in cognitive load or situational awareness in this limited sample. However, recurring instances of mode confusion were observed with hybrid strategies.