EmergingEducation, assessment and learning AI

AI Assessment Specialist

Also called AI Assessment Designer

An AI Assessment Specialist checks that AI-supported tests and tasks measure what they claim to, resist misuse and treat all learners fairly. They give the company evidence it can show customers, regulators and its own team.

What they do

They design assessment tasks and scoring approaches for products where AI generates, delivers or marks work. They define what each task is meant to measure, then test whether it does. They run studies on scoring consistency, compare AI marks with trained human markers, and look at results across learner groups. They review how learners might game or bypass a task using AI. They sit with product, engineering and research teams to turn findings into design changes, and they write up the validation evidence in a form others can check.

What a strong hire delivers

In the first months, a strong hire leaves the company with a clear statement of what each assessment is meant to measure and an agreed way to test it. They produce a first validation study, a map of integrity risks, and a fairness check across learner groups. Product teams change designs because of their findings, and sales and compliance colleagues have evidence they can use.

How you can tell they are good

Ask them to walk through a past assessment they validated, from the intended claim to the evidence. Strong people name the construct, explain how they checked reliability, and say what they changed when results were uncomfortable. They can discuss fairness without slogans. Be wary of candidates who talk only about model accuracy, or who treat agreement with an AI marker as proof of validity.

Why it is hard to fill

The work often sits inside titles such as assessment lead, learning scientist or research scientist, so searching by title finds few people. Psychometricians usually have limited experience with generative systems. Technologists rarely know validation methods. The best candidates mix both, and they are scarce. Many are in education settings and have never been approached by an AI company. Expect to explain the role to them.

Where to find them

Look in assessment organisations, exam boards, education technology companies and university research groups. Useful titles include psychometrician, assessment designer, learning scientist, measurement specialist and learning analytics researcher. Some come from teaching, with a move into assessment design. Others sit in trust and safety, evaluation or applied research teams at AI companies, doing this work under another name.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Assessment Specialist

  1. Tell us about an assessment you validated. What claim were you testing and what evidence did you gather?

    A strong answer names the intended inference, the evidence types used, and the limits of the findings. They describe a decision that changed because of the evidence. Worry if they describe only statistics, or cannot state what the test was meant to measure.

  2. How would you test whether an AI marker scores student writing consistently?

    Look for comparison with trained human markers, repeat scoring checks, clear rubrics, and review of disagreements. They should consider drift and prompt sensitivity. Worry if they rely on a single agreement figure or ignore how the rubric was written.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We build AI-supported teaching and assessment products, and we need someone to make sure our assessments measure what we say they do. You will design tasks, test them, and give us evidence we can trust and show to customers. You will work closely with product, engineering and research colleagues.

What you will do

  • Define what each assessment is meant to measure and the claims we make about it
  • Design assessment tasks and scoring approaches for AI-supported products
  • Run validation studies, including scoring reliability and comparison with human markers
  • Test results across learner groups and report fairness findings
  • Identify ways learners could misuse AI and recommend design changes
  • Write clear validation evidence for internal and external audiences
  • Advise product and engineering teams on assessment decisions

What you will bring

  • Demonstrated work validating or designing assessments, with evidence you can describe
  • Practical command of psychometric methods, including reliability and validity
  • Experience designing studies and interpreting results honestly
  • Understanding of pedagogy and how learners respond to assessment
  • Ability to explain technical findings to non-specialists
  • Comfort working with AI-generated content and AI scoring

Nice to have

  • Experience with learning platforms or tutoring prototypes
  • Background in human-computer interaction
  • Experience assessing risk in AI-enabled products

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Pedagogy and assessmentAdvancedDesign learning activities and valid assessments
  • Human-computer interactionAdvancedDesign and study interactions between people and AI
  • Experimental designWorkingChoose comparisons that identify credible effects
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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