AI is rapidly reshaping the assessment landscape, raising new possibilities for how test content is developed, reviewed, and maintained. But what does this mean in practice for testing programs?
This practical guide explores how AI-driven test development can help programs scale item creation while preserving psychometric rigor and expert oversight. It introduces a structured approach to using purpose-built AI systems across the test development lifecycle, from item generation and SME review to psychometric analysis and item bank updates.
Inside the guide
- Why traditional test development workflows struggle to scale
- How purpose-built AI systems support secure, structured item development
- An 8-step playbook for AI-driven test content development
- How AI improves efficiency while maintaining psychometric rigor
- Key risks and misconceptions to avoid when adopting AI for assessment
- A buyer’s checklist for evaluating AI test development tools
- A real-world licensure program case study demonstrating AI performance and efficiency gains