Paper Number
ECIS2026-2783
Paper Type
CRP
Abstract
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.
Recommended Citation
Tavakoligargari, Masoumeh; Bertram, Matthias; and von Korflesch, Harald F. O., "A Framework For Designing AI-Supported Literature Reviews Under Varying Agency Roles: A Design Science Research Approach" (2026). ECIS 2026 Proceedings. 23.
https://aisel.aisnet.org/ecis2026/litrev/litrev/23
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.
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