Research

Every study, in one place.

Name: The Miraai Open Research Program
Published: 2026

What each study tested, who took part, what it found and what is public, with its DOI. Everything is on the Open Science Framework, and the code for the Qarin digital twins is on GitHub.

Built to the APA/AERA/NCME Standards and the SIOP Principles.

The Qarin digital twins code on GitHub

The Miraai Open Research Program 2026

What it tested: The program that holds every Miraai study, how the studies connect, and the standards each one is run to.
Sample: All studies
What it found: The protocols, reports, analysis logs and results of every study below are linked from one place, and de-identified data and analysis code are shared with approved researchers under a research agreement, except the Qarin digital twin code and 100 released profiles, which are public on GitHub.
Public: Program paper, links to every study, the code for the digital twins · doi:10.17605/OSF.IO/RH3M7

The Miraai Validation Study 2026

What it tested: The personality assessment in its pictured and text formats: structure, reliability, two-week stability, equivalence of the two formats, fairness, criterion relationships, resistance to AI faking, and, in an operational chapter of the report, how it performed with 15,069 real applicants.
Sample: 790 completed forms from working adults, 313 retested at two weeks, 152 who took both formats, and, in the report’s operational chapter, 15,069 applicants under real stakes
What it found: Factor reliability .82 to .93 (α) and .88 to .95 on the model matched to the format; two-week stability .77 to .88; the two formats equivalent (ΔCFI at most .001, no pair with format DIF); no pair beyond the negligible DIF boundary by gender, age or education; Conscientiousness .42 with organizational citizenship, Agreeableness .48 with citizenship toward individuals, Conscientiousness −.42 with counterproductive behavior; first- and second-language applicants within a tenth of a standard deviation on every factor.
Public: The full report, the hypothesis outcomes, the analysis log, the pair statistics and the codebook; de-identified data and analysis code for approved researchers under a research agreement · doi:10.17605/OSF.IO/U693G

The Pilot Before the Pilot 2026

What it tested: Whether Qarin digital twins, each built from one person’s biodata and a written profile of their personality, reproduce what a human pilot finds about new items, and which planted defects they catch. 662 twins were built, and 100 are released publicly on GitHub.
Sample: 662 working adults and their twins, 144 pairs, 116 people on two published inventories, 92 new items with 69 planted defects
What it found: Item means correlate .59 with the people’s, item-total correlations .75, factor reliability reproduces to within .05 (.89 against .89), group differences reproduce almost exactly, the weakest quarter of items is flagged at AUC .85, and the twins catch 33 of 36 planted defects in the critical areas.
Public: The report, the analysis log, the codebook, 100 Qarin profiles and the code to run them on GitHub · doi:10.17605/OSF.IO/PV67F

The AI Panel Against the Experts 2026

What it tested: How an AI rater panel compares with a panel of human experts in keying a situational judgment test, and whether the test keyed with the panel’s help is as sound and as fair as the one keyed by people.
Sample: Both panels rating the same 96 responses, and 9,433 real completions of a leadership judgment test rescored under every key
What it found: The panel’s ratings correlate .84 with the experts’. It rates harder (by 0.57 on a five-point scale) and its raters agree with one another too much, so the key is calibrated for both. The panel-keyed test is as internally consistent as the expert-keyed one, and its items behave the same for men and women, older and younger candidates, and across ethnic groups.
Public: The report as a PDF · doi:10.17605/OSF.IO/NJXF7

Behavioral, Model-Agnostic Integrity in Online Ability Testing 2026

What it tested: Whether watching how a reasoning test is taken, rather than what the answers contain, catches outside help: a field comparison of monitored and unmonitored sessions, and an experiment with a session confidence score.
Sample: More than 106,000 reasoning test sessions in the field, with and without monitoring, and an experiment with 372 participants
What it found: Unmonitored sessions scored higher than proctored ones, most on verbal and numerical reasoning. A confidence score built from how the test was taken, never from what was answered, tells helped sessions from honest ones (AUC .82), and a low score raises a flag for a person to review, never a verdict.
Public: The paper as a PDF; presented at SIOP 2026 · doi:10.17605/OSF.IO/CKNRA

Fast Tests, Expert Judgment 2026

What it tested: How video situational judgment tests are built at scale with industrial-organizational psychologists in charge: the nine-step pipeline, the standard applied at each step, what the psychologist decides, and the research the design rests on.
Sample: The pipeline itself, with its keying step measured against a panel of human experts and 9,433 candidates
What it found: Language models draft at every step and a psychologist makes every decision, written to an audit log with who made it and why. No step opens until the one before it is approved. An eighteen-scenario bank went from incident extraction to an approved draft key, with its faking review, for about twenty dollars in model calls.
Public: The paper, licensed for non-commercial reuse · doi:10.17605/OSF.IO/BQHRA

The Miraai Behavioral Framework 2026

What it tested: A behavioral framework built from published evidence rather than a workshop: the statements, the sources and the structure that came out of them.
Sample: 898 behavior statements from 140 published sources
What it found: Six clusters, eighteen competencies and seventy-seven behaviors, each traceable to its sources.
Public: The framework paper, licensed for non-commercial reuse · doi:10.17605/OSF.IO/NP63V

Research in progress: scoring open answers

We are studying whether candidates could answer Judgment scenes out loud, and whether models can score those answers as consistently as trained experts. This is part of our open research program and is not part of any Miraai product today.

If it becomes one, four things are fixed in advance:

We publish first. The study goes on the Open Science Framework before any client uses it, with agreement against expert raters, validity and group differences.
We monitor it in live use. A share of answers is also scored by trained experts, and agreement and group differences are checked on a fixed schedule. If either drifts, automated scoring stops until it is fixed.
People decide. A score informs the hiring team. It never ranks, screens out or rejects anyone on its own.
Candidates are told when an answer is scored by a model, and the hiring team can always ask for a person to review it.

AI will never make a decision about a candidate.

Where AI is used, and where it isn’t

AI builds and checks our assessments. It does not score, rank or reject candidates in any Miraai product. It drafts and screens items and scenes, predicts structure from item wording, pilots items on digital twins, and informs scoring keys that experts then confirm. Integrity checks raise flags for a person to review and never decide on their own.

Where AI is used, and who decides
Use	Where	What it does	Who decides
Generating content	Every build	Drafts and screens items, scenes and responses, far more than an assessment needs.	A named psychologist approves each stage, and the approval is kept.
Pseudo Factor Analysis	Every build	Reads item wording to predict structure before anyone has answered.	Its read decides which items go to trial, and a human trial follows every time.
Qarin digital twins	Piloting	Simulate responses to find items that fail or are unfair.	No twin result reaches a norm table or a report, and a human trial follows every time.
AI rater panel	Building Judgment keys	Informs the scoring key while the test is built. Experts’ agreement sets the final key.	Experts.
Scoring candidates	All Miraai assessments	Fixed scoring keys and item response models. In Judgment, a candidate’s score comes only from how they rate the responses to each scene, and answering out loud is not part of the test. No AI model scores, ranks or rejects a candidate.	People
Conversational guide	Guidance only	Explains results and answers only from the verified career library.	Hands anything else to a counselor.
Career fit score	Guidance only	A computed profile-match coefficient between the person’s profile and each occupation’s, not a trained model.	Every match shows the traits behind it, and a counselor holds the conversation.
Integrity checks	Reasoning and delivery	Identity check by facial validation, a model-based confidence score from how the test was taken, and optional AI video proctoring, a flag for a person to review, never a verdict.	A person, never the check.

People

Tariq Shaban, Co-Founder and Product and Innovation Director, industrial-organizational psychologist. Master of Applied Industrial/Organizational Psychology, Colorado State University; Master of Strategic Human Resource Management, University of Wollongong.
Dr. Sarah Burke, Co-Founder and Commercial Director, organizational psychologist. PhD in Business Management, Victoria University of Wellington; Master of Psychology (Honours), Massey University; BEd.
Dr. Glenn S. Brown, Co-Founder and Technical Director, chartered psychologist and technologist. PhD in Quantitative Behavioural Psychology, University of Otago; BSc (Hons), First Class, Psychology, University of Otago.

Research collaboration: tariq@miraai.me · llms.txt