Validate·Experimentation·Automation·Developing·VAL-039

Test Plan Drafting

Value hypothesis

AI drafts a structured evaluation plan from a research brief, suggesting appropriate methods, metrics, and potential threats to validity that the researcher then refines and finalises.

Velocity · Quality

Before conducting usability testing or other validation activities, the researcher provides a brief describing the design, the target user, and the evaluation objective. AI generates a draft evaluation plan suggesting appropriate methods, success metrics, sample criteria, and potential threats to the study's validity - such as task ordering effects or missing participant segments. The researcher reviews the draft, adjusts methods to fit real constraints, adds domain-specific criteria AI cannot anticipate, and finalises the plan. Evidence documents cases where AI identified methodological gaps the researcher had not considered, including missing counterbalancing conditions in multi-task studies.

Risks in application

Pseudoproductivity

AI-generated evaluation plans may appear comprehensive while omitting study-specific constraints, ethical requirements, or recruitment challenges that a researcher would catch; a well-structured plan can reduce the scrutiny it receives.

Deskilling

Habitual use of AI to generate evaluation plans may reduce the researcher's own capacity to design studies from first principles, particularly for novel or non-standard research contexts.

Expertise that differentiates

Research and Insight

Evaluating whether AI-suggested methods and metrics are appropriate for the specific design question, or whether they represent generic good practice that does not fit the study's actual constraints, timeline, and user population.

Behavioral Reasoning

Anticipating how participant behaviour during a study may differ from model assumptions, and adjusting the evaluation design to capture the behaviours that actually matter.

AI Fluency that assures

Product Description

Provides a brief describing the design, the target user, and the evaluation objective.

Process Discernment

AI generates a draft evaluation plan suggesting appropriate methods, success metrics, sample criteria, and potential threats to the study's validity - such as task ordering effects or missing participant segments.