From Automation to Collaboration: A Systematic Review of AI Use in Assessment Across Critical Infrastructure Sectors
Abstract
Assessments are used to help gather and analyze information to inform processes and outcomes and are rapidly being reshaped by AI. This systematic review investigates where, why, and when AI is used across the assessment life-cycle and further considers its core functions, design elements, and the ways users engage with them. Thirty-eight years of research studies are our inclusion criteria, each embodying critical intelligence directly into the assessment process. Together, government facilities and healthcare settings accounted for more than 70% of all assessment use cases. Across sectors, the prevailing role of AI was that of a digital assistant, streamlining knowledge capture and evaluation supporting assessment in its role as an expert with a focus on goal-oriented collaboration. These patterns illuminate both the breadth of adoption and the potential of AI as an enhancement tool to partner, offering a roadmap for future assessment design and research.
This systematic review investigates how AI is being used across the assessment life-cycle in critical infrastructure sectors. We examine where, why, and when AI is deployed, along with its core functions, design elements, and user engagement patterns.
Our analysis includes thirty-eight years of research studies, each embodying critical intelligence directly into the assessment process. The findings reveal that government facilities and healthcare settings account for more than 70% of all assessment use cases.
Across sectors, AI predominantly serves as a digital assistant, streamlining knowledge capture and evaluation. This research illuminates both the breadth of adoption and the potential of AI as an enhancement tool to partner with human expertise, offering a roadmap for future assessment design and research.