Cognitive Load Theory Approach to Hybrid Intelligence: Tackling the Dual Aim of Task Performance and Learning

Knowledge workers in information-rich work environments face cognitive challenges as they must deal with multitasking, interruptions, and time pressure. In domains like customer support with high turnover rates and increasingly diverse and complex products, employees need to rapidly develop from novices to experts while showing high task performance. The objective of this paper is to develop design knowledge for hybrid intelligence systems that tackle the dual aim of task performance and learning in knowledge work. We follow a design science research approach and build on theoretical and empirical knowledge on cognitive load theory. We propose a task-user matrix that classifies expertise and task difficulty to identify cognitive challenges. We develop four intervention strategies in the form of design patterns specified through design principles to address these challenges in system design. A pattern evaluation with system developers initially supports the effectiveness, plausibility and feasibility of our patterns.