A Framework For Designing AI-Supported Literature Reviews Under Varying Agency Roles: A Design Science Research Approach

The growing integration of artificial intelligence (AI) into literature reviews raises open questions about researcher-AI collaboration, governance, and the distribution of agency across different analytical tasks. Addressing this gap, this paper develops a design framework based on dual-sourced requirements derived from research on AI-supported literature reviews and from AI governance and principal-agent theory. These requirements inform nine design principles that connect the procedural and epistemic demands of literature review practice with governance imperatives. The principles are instantiated across three agency scenarios: AI as (1) dependent agent, (2) autonomous agent, and (3) principal, reflecting distinct delegation configurations. A prototypical implementation demonstrates the framework’s feasibility and confirms that controlled delegation and human oversight remain essential, particularly for interpretive and theory-oriented tasks. The paper contributes a structured basis for designing responsible AI-based literature review systems across varying autonomy levels.