Help Shape the Future of Graduate Assessment for AI-Enabled Work
RWA is inviting a small number of employers to become Foundation Companies for a new AI-enabled graduate situational judgement simulation.
The simulation measures how graduates make decisions, evaluate AI-generated information, maintain human oversight, communicate uncertainty and escalate risk in realistic workplace situations.
Graduate assessments were not designed for AI-assisted work.
Traditional graduate assessments still have value. They measure reasoning, personality, strengths, motivation and general workplace judgement.
But graduate roles are changing quickly. Early-career employees increasingly use AI tools for research, drafting, summarising, analysis, prioritisation and decision support. Employers now need evidence of how graduates think, check, challenge and decide when AI is part of the workflow.
Can they verify AI-generated evidence?
Graduates need to recognise when AI output is useful, incomplete, unsupported or misleading.
Can they challenge rather than copy?
AI fluency is not enough if candidates accept outputs without critical review.
Can they spot when risk needs escalation?
Early-career employees need judgement about when to ask for guidance, disclose uncertainty or involve a manager.
Introducing the AI-Enabled Graduate Situational Judgement Simulation
The simulation presents realistic early-career workplace scenarios where AI-generated information may be helpful, incomplete, misleading, overconfident, commercially tempting or ethically risky.
Candidates must decide what to trust, what to check, what to challenge, how to communicate uncertainty and when to escalate.
Graduate-relevant scenarios
Built around realistic work involving research, evidence review, client information, communication and decision support.
Psychometric SJT design
Uses structured situational judgement methodology rather than generic AI confidence or self-report questions.
AI governance relevance
Assesses judgement, oversight, risk awareness and responsible AI use in graduate hiring and early-career development.
What the graduate simulation measures
This is not an AI literacy quiz. It assesses how graduates behave when AI becomes part of real work.
AI-Assisted Decision Quality
Using AI outputs to improve decisions without outsourcing judgement.
Information Credibility Evaluation
Checking, challenging and verifying AI-generated evidence before acting on it.
Human Oversight Behaviour
Maintaining meaningful human review rather than passive acceptance of AI recommendations.
Escalation Judgement
Recognising when AI-related uncertainty, error or risk needs manager involvement.
AI Risk Awareness
Identifying commercial, ethical, reputational and operational risks in AI-assisted work.
Confidence Calibration
Balancing trust, scepticism and uncertainty when using AI-supported information.
Foundation Companies help turn a strong assessment concept into a validated graduate product.
The pilot phase is designed to test scenario realism, candidate experience, recruiter usefulness, score interpretation and practical use in real graduate hiring workflows.
What Foundation Companies receive
Early access
Use the graduate simulation before general release and explore how it fits selection, internship, early-career or development priorities.
Input into product design
Provide feedback on scenario realism, construct relevance, reporting usefulness, candidate experience and recruiter outputs.
Graduate AI readiness insight
Gain early insight into how candidates use AI-assisted information, manage uncertainty and maintain appropriate oversight.
Preferential commercial terms
Selected Foundation Companies receive preferential terms in exchange for pilot participation, feedback and anonymised validation data.
What we ask in return
Foundation Companies help create a more realistic, robust and defensible graduate assessment by contributing practical feedback and anonymised validation evidence.
- Pilot the simulation with an appropriate graduate, intern, apprentice or early-career group.
- Provide feedback on scenario realism, candidate experience and recruiter usefulness.
- Support validation and benchmarking activity.
- Share anonymised assessment data for research, refinement and score interpretation.
Organisational and participant information is treated confidentially. Any external use of findings would be anonymised and agreed in advance.
Who this is for
The Foundation Company Programme is relevant for employers where AI is already changing graduate work, early-career recruitment, client delivery, analysis, communication or decision support.
Professional services
Consulting, legal, accounting and advisory firms where graduates use AI to support client research, evidence review and delivery work.
Financial services
Banks, insurers and regulated employers where judgement, verification and escalation are critical in AI-assisted environments.
Retail and consumer businesses
Employers assessing customer, commercial and operational judgement in AI-supported graduate roles.
Technology and data-led employers
Organisations hiring graduates into teams where AI tools are already embedded into everyday workflows.
How the Foundation Company process works
1. Initial conversation
We discuss your graduate population, AI context, recruitment priorities and whether the pilot is a good fit.
2. Pilot design
We agree participant group, assessment format, reporting outputs, timelines and feedback arrangements.
3. Simulation administration
Candidates or early-career employees complete the AI-enabled graduate simulation.
4. Review and refinement
RWA analyses pilot findings, gathers feedback and uses anonymised data to improve the assessment.
Why RWA?
Rob Williams Assessment combines psychometric assessment design, graduate recruitment expertise and AI governance judgement.
The emphasis is not on testing technical AI knowledge. It is on assessing whether graduates can use AI responsibly, critically and effectively in realistic work situations.
25+ years
Psychometric assessment expertise across workplace, education and graduate recruitment contexts.
SJT design
Realistic assessment of judgement, trade-offs, verification behaviour and behavioural intent.
AI hiring defensibility
Focus on structured evidence, responsible AI use, candidate fairness and recruitment decision quality.
Sample — candidate report
AI-Enabled Graduate Simulation
Candidate Report
/100
Overall Graduate AI Readiness Index
Alex demonstrates strong judgement when working with AI-generated information — effective verification behaviour, balanced trust in outputs, and appropriate escalation of risks.
Scale profile
82
80
75
72
84
76
Strengths & development
Strengths
- Challenges unsupported AI-generated claims before acting
- Balances speed and accuracy under pressure
- Demonstrates strong awareness of AI-related risks
Development priorities
- Escalate uncertainty earlier when evidence is incomplete
- Increase documentation of verification steps
- Strengthen confidence calibration in ambiguous situations
Recruiter note
Well suited to graduate roles requiring regular use of AI-assisted research, analysis, reporting, and decision support tools.
Sample — graduate supervisor coaching report
AI Graduate Proficiency Assessment
Line Manager Coaching Report
percentile
Typical of the graduate norm group — an encouraging starting point. Promising instincts around seeking guidance and decision ownership; development needed in risk awareness and principled challenge, which are normal at this stage and highly coachable.
Development priorities
Priority 1 — most urgent
Build basic AI risk habits first
10th percentile on AI Risk Awareness. Agree a simple personal rule: before acting on any AI output that affects another person, pause and ask “What would happen if this is wrong?”
Priority 2
Introduce governance awareness early
Walk through the organisation’s AI use policy together. Make it concrete: which tools are approved, when is sign-off required, and who to consult when an AI output feels risky.
Priority 3
Build habit of questioning AI outputs
Normalise challenge explicitly: “I expect you to ask questions about AI outputs, not just accept them.” Set a weekly task — find one output to question and report back.
Early strengths
Supervisor note
Development gaps in this profile are normal at graduate level and respond well to structured coaching. Most graduates develop these capabilities within 6–12 months with the right support.
Frequently asked questions
Is this an AI literacy test?
No. AI literacy tests usually assess knowledge, confidence or familiarity. This simulation assesses judgement, oversight, verification behaviour and decision quality in realistic graduate workplace situations.
Who should take part in a pilot?
Graduate applicants, interns, apprentices or early-career employees whose future roles are likely to involve AI-supported research, analysis, communication, decision-making or client work.
Can the simulation support recruitment decisions?
Yes. The simulation is designed to provide structured assessment evidence that can support graduate recruitment, early-career selection, development and benchmarking.
How is pilot data used?
Pilot data is used to refine the assessment, improve reporting, review item performance and support validation. Any external reporting would use anonymised data and be agreed in advance.
Become a Graduate Foundation Company
Foundation Company places are limited and participation is by application.
If your organisation is rethinking graduate assessment for an AI-enabled future, we would welcome a conversation.