Applying Job Design Criteria for Effective Human-AI Collaboration

Abstract

Human-AI collaboration often underperforms due to a lack of motivation-supportive system design. This paper proposes a framework grounded in work design theory – specifically the Job Characteristics Model (JCM) – to guide the development and evaluation of AI systems. We introduce qualitative evaluation anchors that translate core job design criteria into assessable aspects of AI-supported work. These anchors were developed through a theory-driven process that combines work design theory with recent literature on AI’s impact on work characteristics. The goal is to foster intrinsically motivating and cognitively engaging human roles in AI collaboration, thereby enhancing overall human-AI team performance.

Keywords: Human-AI Interaction Design, Job Characteristics Model, Intrinsic Motivation, Evaluation Anchors, Work Design

DOI: 10.54941/ahfe1006695