Everyday AI Judgment at WorkYou use AI every day. Is your judgment as good as your prompts?
A scored diagnostic of the skill employers actually screen for in the AI era — knowing when to trust an output, what never to paste into a tool, which tasks fit AI and which do not, and when to say AI was used.
Not a prompt quiz — a judgment test
Most AI-literacy tests check whether you know what a token or a hallucination is. This one puts you inside 20 workplace situations where a capable professional could reasonably go either way — a confident summary you did not verify, a client file that would make a prompt more useful, a deadline an unchecked draft would save — and scores the choices you would actually make.
Scoring uses a Trust Calibration Index: every option carries a hidden reliance weight, scored separately from answer quality. The report tells you whether you run on Autopilot (over-trusting), as a Holdout (under-using), or Calibrated — the profile hiring managers say they want and cannot find.
A risk-hygiene lamp tracks stewardship and transparency separately, so one careless paste or one undisclosed AI draft is named as exactly what it is — before it happens at work.
What you walk away with
Autopilot, Holdout, or Calibrated — with the evidence behind it. Not how much you use AI, but whether your trust in it tracks the situations where it deserves trust.
Where you check before you ship and where you do not — the single behaviour that separates safe AI users from the ones who make the news.
How you handle the confidential, the personal and the proprietary around AI tools, scored against the rules most companies now write into policy.
Which kinds of work you correctly route to AI, which you correctly keep, and where your instincts are miscalibrated in either direction.
Whether you disclose AI use when it matters — to your manager, your client, your reader — keyed to emerging professional norms.
Inside your report
Illustrative sample — your report is generated from your own responses.
Built for
- Knowledge workers who use AI tools daily and want proof of safe, skilled use
- Job seekers who want an AI-judgment credential employers can read in one page
- Teams and L&D leads rolling out AI tools who need a baseline of judgment, not hype
Find out if your AI judgment is calibrated
33 scored exercises · about 35 minutes · full bespoke report with your Trust Calibration Index.
₹999 (incl. GST) · assessment and full report, nothing further to pay
Frequently asked questions
Four competencies: verification discipline, information stewardship, task-fit judgment, and transparency and accountability. It measures what you would actually do in 20 realistic workplace situations, plus select-all and evidence-reading exercises — not what you know about AI theory.
Every scenario option carries a graded quality score and a separate hidden reliance weight. The reliance weights build your Trust Calibration Index — Autopilot, Holdout, or Calibrated — while quality scores build your competency profile. A risk-hygiene lamp flags stewardship and transparency issues separately.
Any knowledge worker who uses AI tools in email, documents, spreadsheets or code — individual contributors, managers, and job seekers who want evidence of safe, productive AI use. No technical background is needed.
About 35 minutes for 33 scored exercises. You get a full report with your Trust Calibration Index, per-competency scores, your risk-hygiene reading, and specific guidance — downloadable as a colour PDF.
₹999 in India (including GST) or US$9.99 elsewhere, as a one-time purchase for one full sitting and report. Organisations can run it for teams through AssessAll credits.
Each one takes a single capability, puts you inside the situations where it is actually tested, and scores your choices against published evidence — with a report designed for that capability alone, not a template. They span hiring, compliance, education, operations and personal skill.
Browse the catalogue →Methodology: Measures applied judgment in everyday use of generative AI tools through original situational items keyed to published evidence on automation bias and complacency (Parasuraman & Manzey, 2010; Skitka, Mosier & Burdick, 1999), trust calibration in automation (Lee & See, 2004; Hoff & Bashir, 2015), algorithm aversion and appreciation (Dietvorst, Simmons & Massey, 2015; Logg, Minson & Moore, 2019), the jagged-frontier field experiment on knowledge-worker productivity with AI (Dell'Acqua et al., 2023), hallucination and citation-fabrication findings in large language models (Ji et al., 2023 survey; Walters & Wilder, 2023), the NIST AI Risk Management Framework (2023), ISO/IEC 42001 AI management principles, the EU AI Act transparency obligations, data-protection guidance on confidential inputs (ICO and CNIL guidance on generative AI), and the information-security literature on shadow IT. Trust Calibration Index is an original scoring design: every option carries a signed reliance weight, aggregated separately from the quality key so that over-reliance and under-use are reported as distinct tendencies. Items are original works and no commercial instrument is reproduced.