Beyond automation: how agentic AI is reimagining test content development

Artificial intelligence has quickly moved from concept to capability. Across industries, it’s reshaping how complex, expert-led processes are designed and delivered. But while much of the discussion has focused on automation, the real opportunity lies in reimagining how expertise is captured and applied.

In test development, AI’s power extends far beyond accelerating item generation. It’s changing how assessments are redesigned, validated, and scaled. AI is enhancing rather than replacing human expertise and preserving critical knowledge that has traditionally been scattered across documents, workshops, and individual experts’ heads.

Agentic AI, the next evolution of artificial intelligence, represents a shift from simple task automation to intelligent, goal-directed systems that collaborate with human experts. This means rethinking every stage of test development – from blueprint alignment and item generation to psychometric validation and item bank maintenance – within an adaptive, transparent and defensible framework.

Traditional test development under pressure

For decades, assessment programmes have relied on expert-led, resource-intensive models of test development. And as demand for professional qualifications has grown, so too has the strain on item banks and the subject matter experts (SMEs) who devote valuable time to building and maintaining them.

Much of this pressure stems from how knowledge is managed. Standards, rationales, reference interpretations, and psychometric decisions are often spread across documents, emails, and individual contributors. When programmes scale, refresh content, or experience SME turnover, that institutional memory can fragment or be lost altogether.

This creates a growing challenge: how to meet increasing demand for secure, fair, and valid assessments without sacrificing quality, overburdening SMEs, or continually relearning what is already known.

In response, many organisations have turned to generative AI as a potential solution. However, most commercial tools are designed for general language tasks, not for the psychometric precision required in high-stakes testing. Wrapping these tools around existing workflows introduces new risks, including inconsistent quality, limited traceability, and a lack of defensibility.

The challenge isn’t whether AI can generate text. It’s whether it can generate assessment content that is psychometrically sound, bias-aware, and aligned with a defensible blueprint.

Redefining content generation with agentic AI

Agentic AI offers a fundamentally different model. Rather than automating isolated tasks, it creates systems of interconnected, purpose-built agents working together under human oversight. Each agent has a defined role, either generating, refining, or analysing content while sharing context across the workflow.

Together, these agents form a structured institutional memory. Decisions, standards, feedback, and refinements are not lost between cycles. They are captured, retained, and applied over time.

In the context of test development, this approach means:

• Smarter generation – Specialised AI agents create high-quality items aligned to approved references and blueprints.
• Human oversight built in – SMEs review, refine, and approve content, with each interaction strengthening future outputs and performance.
• Defensible outputs – Every item links back to its source, creating a clear audit trail for validity and fairness.
• Secure inputs – All materials, such as blueprints, reference materials and item writing guidelines, remain protected in a closed system.
  Continuous learning – The system adapts with each cycle, improving clarity, psychometric strength, and efficiency over time.

Real-world results from a PSI pilot

PSI’s AI test development approach uses purpose-built agentic AI, designed specifically for assessment programmes. Developed collaboratively by assessment scientists and AI engineers, it integrates psychometric best practice into every stage of the workflow – from AI generation and SME review to validation and item bank maintenance.

In a recent large-scale insurance licensing pilot, this approach demonstrated how agentic AI can deliver measurable improvements without compromising rigour:

77.4% of AI-generated items met psychometric thresholds, compared to 75.5% of human-authored items.
65.6% of AI-generated items were approved for pretesting following SME review.
SMEs retained 10% more AI-generated items across review batches, reflecting system learning and improved quality.
The project also produced thousands of additional items beyond target, achieving substantial time and cost efficiencies. Clear evidence of how AI innovation and psychometric design can scale together.

AI as a catalyst for ecosystem innovation

The same agentic framework is already driving innovation in other parts of the assessment lifecycle.

In PSI’s AI-driven test preparation platform, more than 50,000 AI-generated items are already live. These support adaptive quizzes, aligned study materials, and a personalised AI learning assistant that helps candidates focus on areas of need.

Insights gained in this environment can inform broader programme improvement. Patterns in candidate engagement, areas of common misunderstanding, and feedback on content clarity help strengthen future development decisions.

For assessment organisations, it means AI is no longer a tool used at one point in the process. It’s an enabler of innovation across the entire assessment lifecycle.

The future of human + machine test design

The future of test development is not a story of AI replacing humans, but of humans and machines advancing together.

Agentic AI marks a turning point, moving from reactive automation to proactive collaboration. It empowers experts to focus where they add the most value: applying judgment, validating quality, and ensuring fairness, while the system preserves and applies that expertise consistently and at scale.

As assessment leaders look to the future, the challenge is not whether to adopt AI, but how to adopt it responsibly, with transparency, security, and psychometric integrity at the core. With agentic AI, that future is already taking shape. A future where innovation and integrity evolve together to define the next era of assessment design.