This page documents how DataU Upskillers assessments are designed, scored, and interpreted. It exists to enable independent verification by employers, universities, government bodies, and international organisations.
Validity Statement
DataU Upskillers assessments are formative, framework-aligned indicators for career development planning. They have not yet undergone criterion-related validity studies (predicting job performance), Differential Item Functioning (DIF) analysis, or ISO 17024 personnel certification audit. Scores should be used as one signal among many in hiring or admissions decisions — not as standalone criteria.
Every question in our assessments is aligned to specific competency codes in published international standards. This provides content validity evidence — the questions cover the domain they claim to measure.
| Framework | Reference | Role in our assessment |
|---|---|---|
| DigComp 2.2 (EU, 2022) | doi.org/10.2760/115376 | Primary content validity reference. Questions aligned to specific area codes (1.1–5.4) and proficiency levels (1–8). |
| OECD AI Competency Framework (2025) | ailiteracyframework.org | Basis for PISA 2029 AI literacy module. Used for domain categorisation (Engage/Create/Manage/Design). |
| UNESCO AI Competencies for Students (2024) | UNESCO.org | 12 student competencies covering ethical AI, safety, and human-centred skills. Referenced for ethics and risk questions. |
| Meta AI Literacy Scale (MAILS) | Carolus et al., 2023 | Psychometric inspiration for item design. Not a licensed implementation. |
| Framework | Reference | Role in our assessment |
|---|---|---|
| NACE Career Readiness Competencies (2021) | naceweb.org | Primary employer-defined competency taxonomy. All 8 NACE competency areas (CA_1–CA_8) covered across the full assessment. |
| O*NET Content Model v29.0 (2025) | onetcenter.org | Occupational competency element IDs (e.g., 4.A.4.b.2) cited per question. Provides traceability to DOL employment data. |
| SHRM Behavioral Competency Framework (2022) | shrm.org | 9 HR-professional competencies aligned to our soft skill categories. |
| PMI Power Skills (2023) | pmi.org | Project management profession alignment for leadership and problem-solving questions. |
AI Literacy (v3, 2026)
Power Skills (v3, 2026)
Bias controls: Distractors are written to be plausible (not obviously absurd) to prevent test-wiseness effects. Scenarios avoid role, gender, or cultural stereotypes. Cultural relevance for SEA contexts (hybrid teams, AI adoption, cross-cultural work) is embedded in scenario framing.
The Talent Index is a composite index (0–100) combining four empirically-grounded sub-factors. It is not a validated psychometric instrument — it is a transparent, auditable career readiness indicator.
Formula:
Core = (Si + Cx + Eq) / 3
TalentIndex = Core × Dampener(Av)
Dampener(Av) = 1 / (1 + (Av/75)^1.8), floor 0.38
Si — Social Intelligence: log-scale proxy from CV soft-skill count; replaced by O*NET avgTaskSocial when Atlas is available. Source: O*NET Research Center; Thorndike (1920); Cantor & Kihlstrom (1985).
Cx — Cognitive Complexity: log-scale proxy from skill breadth; replaced by O*NET avgTaskCognitive. Source: Savickas & Porfeli (2012) Career Adapt-Abilities Scale.
Eq — Emotional Quotient: formal Power Skills assessment score (primary); log-scale CV proxy if unavailable. Source: Bar-On EQ-i model (2006); Goleman (1998).
Av — Automation Velocity: O*NET/Atlas task automation percentage (primary); LLM risk proxy (secondary). Source: Brynjolfsson & Mitchell (2017); Frey & Osborne (2017).
Equal weighting of Si, Cx, Eq: justified by Dawes (1979) no-differential-weights principle — appropriate in absence of calibration data.
Scores map to the ASEAN Qualifications Reference Framework (AQRF) — the 8-level regional standard used by all 10 ASEAN member states for qualification equivalence. This mapping makes scores meaningful to SEA employers, governments, and universities.
Score
0–24
AQRF 1Foundational — 1–2 (Foundation)
Requires substantial AI onboarding; suitable for roles with minimal AI exposure
OECD level: Engage: Recognise
Score
25–44
AQRF 2Developing — 2–3 (Foundation→Intermediate)
Can use mainstream AI tools (ChatGPT, Copilot) for routine tasks with guidance
OECD level: Engage: Understand
Score
45–59
AQRF 3Competent — 3–4 (Intermediate)
Independently uses AI tools; can automate routine workflows; needs oversight for high-stakes outputs
OECD level: Engage: Apply + Create: Assist
Score
60–74
AQRF 4Proficient — 4–5 (Intermediate→Advanced)
Strong AI collaborator; can lead team AI adoption; ready for AI-augmented professional roles
OECD level: Create: Produce + Manage: Govern
Score
75–87
AQRF 5Advanced — 5–6 (Advanced)
AI champion / project lead; can design and implement AI solutions for business problems
OECD level: Create: Produce + Design: Architect
Score
88–100
AQRF 6Expert — 7–8 (Highly Specialised)
AI strategist / technical lead; defines organisational AI roadmap; CTO/AI Director adjacent
OECD level: Design: Innovate + Create: Lead
SEM = SD × √(1 − α) (Allen & Yen, 1979). A score of 72 with SEM=6 means the user's true score falls in 72 ± 6 with 68% probability, or 72 ± 12 with 95% probability. Displayed on all score pages.
Current reliability data
Cronbach's alpha is computed from live response data as the platform grows. Minimum N=20 complete response matrices required for meaningful α. Current status and live reliability statistics are available at /api/assessments/reliability.
Target: α ≥ 0.75 for AI Literacy (formative use); α ≥ 0.75 for Power Skills (formative use). Future goal: α ≥ 0.80 for professional development, α ≥ 0.90 for high-stakes screening.
Employers
Universities
Governments & Ministries
Phase 1 (Current)
Phase 2 (N≥300/country)
Phase 3 (12–24 months)
For validation partnerships, concurrent validity studies, government MOU discussions, or employer integration queries, contact our research team:
DataU Upskillers Assessment Methodology · v3 · Published 2026-01-01 · Phnom Penh, Cambodia