Human–AI Collaboration Assessment
Measure whether employees and leaders make better, safer and more defensible workplace decisions when AI contributes evidence, recommendations, summaries or analysis.
Explore the assessment
Are your people genuinely collaborating with AI — or simply accepting its first answer?
AI can produce fluent, detailed and apparently authoritative outputs. Collaboration quality depends on whether people identify missing evidence, test assumptions, recognise uncertainty and retain ownership of the final judgement.
- Realistic workplace decision simulations
- Role-relevant behavioural evidence
- Bespoke capability frameworks
- Individual, team and organisation reporting
AI adoption creates a human–AI collaboration gap
Organisations are moving quickly from experimentation to routine AI-assisted work. Access, training and confidence provide limited evidence that people can use AI without weakening judgement or accountability.
Why access to AI does not equal effective collaboration
Generative AI can make weak reasoning appear complete.
- Unsupported conclusions can appear authoritative
- Important caveats may be hidden or absent
- Speed can reduce verification and challenge
- Responsibility can drift from the person to the tool
What effective collaboration looks like
High-quality AI-assisted decisions combine AI’s value with active human judgement.
- Uses AI selectively rather than automatically
- Verifies in proportion to impact and uncertainty
- Challenges assumptions and overconfident claims
- Records a defensible rationale for the final decision
How the Human–AI Collaboration Assessment works
Participants respond to realistic situations in which AI contributes useful but incomplete, uncertain or potentially misleading information to a workplace decision.
Realistic work task
A role-relevant task where AI could add value, such as analysing evidence or preparing a recommendation.
Collaboration choices
The participant decides whether to use AI, what context to provide, and when human expertise should lead.
Critical interaction
They evaluate, challenge and improve AI outputs, and test whether the result is fit for purpose.
Collaboration profile
Responses provide structured evidence of how effectively AI and human judgement are combined.
What the assessment can measure
The exact framework reflects the target roles, decisions and AI use cases.
Appropriate AI Use
Recognises when AI adds meaningful value, when it offers marginal benefit, and when human-only work is more appropriate.
Context Provision
Defines the objective, audience, constraints and organisational context needed for a useful AI response.
Iterative Refinement
Treats the first response as a starting point and improves the output through purposeful iteration.
Output Challenge
Identifies weak assumptions, unsupported claims and overconfidence in AI-generated work.
Option Generation
Uses AI to broaden thinking and surface alternatives without letting it narrow the decision prematurely.
Human Oversight Behaviour
Remains actively engaged and avoids allowing responsibility to drift from the human user to the system.
What a human–AI collaboration simulation can look like
Example only — not a live scored item.
You are reviewing an AI-generated recommendation to change the allocation of specialist staff across three client projects. The model predicts improved utilisation and margin, but the summary does not explain which historical data were used. One project director supports immediate implementation; another warns the recommendation may overlook client-specific knowledge. You must advise whether the proposed allocation should proceed this week.
What the response reveals
- Whether the strongest response applies proportionate verification
- Whether useful momentum is preserved rather than stalled
- Whether accountable human judgement is retained throughout
Collaboration profiles that support action
Reporting can be designed for recruitment, individual development, leadership programmes or governance assurance.
Provides clear context before requesting AI support and integrates AI assistance with relevant human expertise; development priority is challenging confident outputs before incorporating them.
Scores and descriptors are illustrative. Norms, benchmarks and interpretive claims should be based on evidence for the relevant assessment version, population and intended use.
How this differs from other AI products
AI literacy or prompt-skills training
Measures knowledge, confidence and tool familiarity — not how a person responds to an uncertain, consequential decision.
A governance audit
Reviews policies and controls. This assessment examines whether people demonstrate the behaviours those controls depend upon.
Self-report readiness surveys
Self-report can indicate confidence or perceived capability. This assessment provides structured evidence of choices under realistic trade-offs.
Psychometrically informed and evidence-led
Recognised standards inform the design and use of this assessment. They are not presented as proof that a particular version has already been validated — reliability, validity, fairness and benchmark evidence should be developed through piloting and appropriate use.
ISO 10667-1 & 10667-2
International standards addressing responsibilities, procedures and quality considerations when assessment services are used in work settings.
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ISO/IEC 42001:2023
The international AI management-system standard, covering governance, policies, accountability and risk management.
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NIST AI Risk Management Framework
A voluntary framework helping organisations govern, map, measure and manage risks associated with AI systems.
OECD AI Principles
International principles promoting innovative and trustworthy AI, including transparency, robustness and accountability.
SIOP Principles
Professional principles addressing job relevance, validation, fairness and responsible use of employment assessment procedures.
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Standards for Educational & Psychological Testing
Widely recognised guidance on validity, reliability, fairness, score interpretation and appropriate assessment use.
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Independent psychometric expertise for an emerging business risk
RWA brings over 25 years of assessment design experience to the challenge of measuring how people collaborate with AI and make accountable decisions.
Collaboration-focused, not tool-focused
Focuses on transferable collaboration behaviours rather than proficiency with a platform that may soon change.
Bespoke rather than generic
Frameworks and scenarios reflect the organisation’s actual roles, decisions, risks and governance expectations.
Behavioural rather than self-report
Participants respond to realistic trade-offs, providing more direct evidence than confidence ratings.
Commercially relevant and defensible
Outputs support hiring, development and governance without overstating what the evidence can establish.
Explore related RWA AI assessment services
Build a Human–AI Collaboration Assessment for your organisation
Discuss the target population, high-value decisions, AI use cases and reporting needs with Rob Williams Assessment.
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Human–AI Collaboration Assessment FAQs
What is effective human–AI collaboration?
Using AI selectively and purposefully while supplying context, refining outputs, challenging weaknesses and retaining responsibility for the final work.
How is this different from an AI literacy test?
AI literacy concerns knowledge and understanding. This assessment focuses on applied behaviour when AI is part of a real decision.
Does it test prompt-writing skill?
Prompting may form part of a simulation where relevant, but it is not the complete capability being assessed.
Which employee groups can be assessed?
Graduates, professionals, managers, functional directors or executives, with content matched to their real responsibilities.
Can it be used in recruitment?
Yes, where collaboration capability is relevant to the role, alongside a job analysis and fairness review.
Can it be used for workforce development?
Yes. It can establish a capability baseline and guide learning priorities.
What formats are available?
Best-and-worst judgement, response ranking, multi-stage scenarios, branched simulations and document or video-based situations.
How do we begin?
Identify the population, intended use and important AI-assisted decisions; RWA can then propose a framework and development process.