EDi Verified · Methodology
How EDi credentials are assessed
An EDi Verified credential says that a person demonstrated, on realistic work tasks, with a live AI assistant and under assessment conditions, that they check AI output against the source and keep sensitive data out of AI tools. This page explains how that is measured, how a pass is decided, what a retake means, what the versions on a certificate refer to, how we check the assessment measures what it claims to, and the research the design draws on.
- 8 criteria
- Four levels each, rubric edi-v2
- Live AI
- Planted problems and lures
- 3 scored attempts
- 24 hours apart, fresh scenario
- Checked nightly
- Against human-scored cases
01
The assessment method
Every scored lesson is a work task set in a fictional organisation: a brief, a set of source materials (a policy, an email thread, a spreadsheet, a report) and an AI assistant the participant works with in a chat. The participant produces a deliverable they would stand behind at work.[14]
- 1
Work with a real AI
The assistant is a real model, not a script. It is kept blind to the planted problems and the scoring notes, so the participant gets no help it would not get at work.
- 2
Catch what it gets wrong
The materials hold planted problems: a figure the AI will misstate, a rule it will overlook, sensitive data it should not be given. Lures look suspicious but are correct, so flagging everything does not pay.
- 3
Scored, then questioned
The transcript and deliverable are scored against the rubric by an automated judge, run several times and reconciled. On challenge lessons the participant then answers questions about their own work, with it out of view.
Whether the participant notices, checks and corrects what the AI gets wrong is the thing being measured. Each own-words question asks for a specific value from the record; answers that do not match it lower the verification score. Borderline results go to a person.
02
The eight criteria
Each attempt is scored on eight criteria at four levels. Four describe how the participant worked with the AI, four how they reviewed the result. Two scores come out of this, working with the AI and reviewing the result, each 0 to 100, with the overall score as their mean.
Working with the AI
- 1
Gave the AI the context it needed
Strong: Gave complete, well-chosen context up front and kept the AI anchored as the work progressed.
- 2
Asked clearly for what they wanted
Strong: Precise, well-structured requests that set goal, format, tone and constraints.
- 3
Pushed back on or corrected the AI
Strong: Consistently challenged claims, asked for reasoning and corrected mistakes.
- 4
Iterated to improve the result
Strong: Deliberate iteration that clearly shaped the final result.
Reviewing the result
- 5
Checked the AI's facts against the material
Strong: Systematically verified facts, names, dates and figures against the material.
- 6
Applied the rules and requirements correctly
Strong: Applies every relevant rule correctly and explains it plainly where needed.
- 7
Caught and handled the planted problem(s)
Strong: Caught and correctly handled every planted problem. Derived from the planted problems caught and handled, not judged separately.
- 8
Final deliverable is fit to use
Strong: Ready to send or use as is: answers the ask in the right tone.
03
The decision rule
A credential is earned by passing every lesson of its level. A lesson passes when its score of record meets the pass mark and, where set, its hurdles.
Hurdles cannot be made up for elsewhere
- Checking the material: at least Developing on the verification criterion.
- Sensitive data: every planted problem that involves it must be caught.
These are conjunctive requirements: a high score on other criteria does not compensate for missing them.[3, 7]
The level's challenge lesson, taken under assessment conditions, is judged on both dimensions: working with the AI and reviewing the result must each meet the certification's mark, and every planted problem must be caught. Participants are also expected to show the standard on their own: the mean of first attempts across the level must meet a floor, and a dimension score within a few points of its mark, over or under, is reviewed by a person before the credential is issued or refused. An attempt the integrity checks flag waits for a person as well.
| Rule | In short |
|---|---|
| v1 | Every lesson at its pass mark (overall score), best attempt counting. |
| v2 | Pass mark plus a floor on checking the material and on keeping sensitive data out; challenge lessons judged on both dimensions; bounded retakes; borderline results reviewed by a person. |
Each certificate records which rule produced it.
04
The retake policy
- 3 scored attempts
- per lesson on credentialed levels; further attempts are practice
- 24 hours apart
- between scored attempts
- A fresh scenario
- every retake: same objectives and planted structure, new story
A second attempt is a second demonstration, not a second look at the same page. Practice attempts are built, scored and fed back in full, but sit outside the score of record. The entry-level credential, AI Baseline, keeps unlimited scored attempts with the best one counting.
The rationale: a bound on scored attempts keeps a pass from being the product of persistence alone, the gap between them gives learning a chance to happen,[2] and fresh scenarios mean memorising a layout earns nothing. Practice stays open because retrieval practice with feedback is itself one of the most reliable ways to learn,[12] and the certificate separately records how many attempts were used and the first-attempt score, so a reader can see how the standard was reached.
Credentials with an expiry date are renewed by passing the level's challenge lesson again. Holders are told 60 days before expiry; the retake is free, earlier attempts do not count against the limit, and a pass issues a new certificate under the rule in force at the time. Nobody is required to renew: an existing certificate stands until its expiry date.
05
Versioning
Two versions are stamped on every certificate and never change afterwards: the rubric version (what was scored and how the levels are defined) and the decision-rule version (how scores became a pass). A certificate issued under v1 remains a valid v1 certificate; issued credentials are not re-scored or re-decided when the rule changes. Lessons passed before a rule change keep counting; new scored attempts follow the rule in force from that moment.
| v1 | v2 | |
|---|---|---|
| Lesson pass | Overall score at the pass mark | Pass mark plus hurdles |
| Challenge lesson | Overall score | Both dimensions at the mark, every planted problem caught |
| Retakes | Unlimited, best counts | 3 scored, 24 hours apart, randomised scenarios |
| Lures | None | Included |
| Own-words questions | With the work in view | With the work out of view |
| Effort and integrity checks | None | Run on every scored attempt |
| Borderline and flagged results | Decided automatically | Reviewed by a person |
Holders of a v1 credential are offered a free challenge retake under v2; the v1 credential is not withdrawn.
06
The evidence program
We treat the assessment as a measurement and check it as one, following the argument-based approach to validation: state what a pass is meant to show, then collect evidence for each link in that claim.[1, 9]
Nightly calibration
The automated judge is run every night against a bank of cases with known correct scores. Agreement is tracked overall, per criterion and per category of planted problem; the team is alerted when a criterion falls under 70% exact agreement or the judge starts to drift.
Human second scoring
A sample of real attempts, stratified by score band and weighted towards borderline and flagged ones, is scored independently by two trained people with an adjudicator. Human-to-human and human-to-judge agreement are reported, and resolved cases join the calibration bank.
De-identified export
Attempt-level data for validity work (levels for every judge run, planted hits and misses, lure false alarms, effort measures, attempt number and outcome) can be exported with stable pseudonyms in place of identities.
Review after every rule change
Pass-rate shifts, lure false-alarm rates and the share of attempts flagged by the integrity checks are reviewed whenever the decision rule changes.
07
Research basis
The design choices above draw on established measurement practice and on research into how people work with automated systems. These sources support the design; evidence that EDi's own scores mean what they claim comes from the evidence program above.
- Validity as an argument
- A score is valid for a stated use when evidence supports each step from the performance to the decision. The evidence program is organised that way.[1, 9, 10]
- Realistic work tasks
- Performance on authentic tasks, with real materials and a real tool, represents the skill more fully than recall questions about it.[10, 14]
- Why checking is the core skill
- People over-trust automated output, and AI help can lower the quality of work on tasks the AI handles badly. Catching AI errors is therefore what the credential certifies.[4, 5, 11]
- Planted problems and lures
- Catching real problems while leaving correct details alone is a detection task. Counting both hits and false alarms separates real discrimination from flagging everything.[6, 13]
- An AI judge, run several times
- Language-model judges agree well with people on many tasks but show known biases, so judgements are repeated, reconciled and checked against human scores.[16]
- Checking the judge against people
- Automated scores are evaluated by exact and adjacent agreement with human raters, compared with how well human raters agree with each other.[15]
- Hurdles and pass marks
- Some requirements are conjunctive by design: strength elsewhere should not compensate for missing them.[3, 7]
- Spaced, bounded retakes
- Spacing between attempts improves learning, and practice with feedback strengthens it; bounding scored attempts keeps the pass decision meaningful.[1, 2, 12]
- Certification structure
- Published scope, a documented decision rule, versioned criteria, renewal and a public verification page follow the elements expected of a scheme that certifies persons. EDi is not accredited to this standard.[8]
08
References
- 1.American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for Educational and Psychological Testing. Washington, DC: AERA. www.testingstandards.net/open-access-files.html
- 2.Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. doi.org/10.1037/0033-2909.132.3.354
- 3.Cizek, G. J., & Bunch, M. B. (2007). Standard Setting: A Guide to Establishing and Evaluating Performance Standards on Tests. Thousand Oaks, CA: Sage. doi.org/10.4135/9781412985918
- 4.Dell'Acqua, F., McFowland, E., Mollick, E. R., et al. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper 24-013. doi.org/10.2139/ssrn.4573321
- 5.Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. doi.org/10.1136/amiajnl-2011-000089
- 6.Green, D. M., & Swets, J. A. (1966). Signal Detection Theory and Psychophysics. New York: Wiley.
- 7.Haladyna, T., & Hess, R. (1999). An evaluation of conjunctive and compensatory standard-setting strategies for test decisions. Educational Assessment, 6(2), 129–153. doi.org/10.1207/S15326977EA0602_03
- 8.ISO/IEC 17024:2012. Conformity assessment: General requirements for bodies operating certification of persons. Geneva: International Organization for Standardization. www.iso.org/standard/52993.html
- 9.Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. doi.org/10.1111/jedm.12000
- 10.Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749. doi.org/10.1037/0003-066X.50.9.741
- 11.Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. doi.org/10.1177/0018720810376055
- 12.Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. doi.org/10.1111/j.1467-9280.2006.01693.x
- 13.Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137–149. doi.org/10.3758/BF03207704
- 14.Wiggins, G. (1990). The case for authentic assessment. Practical Assessment, Research, and Evaluation, 2(2). doi.org/10.7275/ffb1-mm19
- 15.Williamson, D. M., Xi, X., & Breyer, F. J. (2012). A framework for evaluation and use of automated scoring. Educational Measurement: Issues and Practice, 31(1), 2–13. doi.org/10.1111/j.1745-3992.2011.00223.x
- 16.Zheng, L., Chiang, W.-L., Sheng, Y., et al. (2023). Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. Advances in Neural Information Processing Systems 36, Datasets and Benchmarks Track. arxiv.org/abs/2306.05685
To check that a certificate is real, use the verification page.