Human-Driven AI AI Capability Assessment
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Personal assessment

Map your AI capability.

A guided assessment that turns your current AI practice, human strengths and readiness into a clear pathway.

Assessment map4 steps

This assessment maps current AI use, transferable human strengths and readiness to implement a small AI-supported workflow.

Personal assessment

Map your AI capability.

A guided assessment that turns your current AI practice, human strengths and readiness into a clear pathway.

1. Your details

Add the basic context for your named assessment report.

Score guide

Click a score stage on the right to open its explanation here.

2. Current AI practice

This section looks at how you use AI tools, how clearly you instruct them, and how consistently you review what they produce.

3. Human strengths

Select strengths that already show up in your work, learning, volunteering, caring, business or creative life.

1
Choose a category
2
Select strengths
3
Move to the next category and repeat
No strengths selected yet.

4. Your readiness

This section looks at how ready you are to take one useful AI-supported activity from intention to action.

Your result

AI capability report

Your report will appear here.

Capability dashboard

A clearer view of the signals behind the final score.

Human advantage

How much judgement, lived context and human capability sit behind your AI use.

Signal
0Awaiting inputs

Future-proofing signal

How resilient your strengths mix looks in an AI-changing world.

Building
0Awaiting inputs

Readiness signal

How close you are to trying one useful AI-supported activity in real life.

Building
0Awaiting inputs

Recommended pathway

Four-week pathway

A practical route from reflection to evidence.

Action cards

These cards support the four-week pathway. They give you practical prompts to use while you test and refine your AI workflow.

Aligned to pathway

Continue with AI

Copy this prompt into ChatGPT after saving your PDF. It extends the assessment into a tailored development plan.

Methodology note

This assessment is an indicative self-assessment tool developed by Human-Driven AI. Its scoring model is informed by recognised digital competence, employability and future-skills frameworks including DigComp 2.2, Skills Builder and OECD Learning Compass principles. It is designed for reflection, pathway planning and capability development. It is not an accredited qualification or psychological diagnostic tool.

© Human-Driven AI CIC, 2026. Indicative assessment for reflection, pathway planning and capacity development. This assessment runs in your browser. Your answers are not submitted or stored by Human-Driven AI unless you choose to share them.
Methodology

How this assessment is scored.

This page explains the scoring model behind the Human-Driven AI capability assessment. The model is an indicative HDAI self-assessment, informed by recognised digital competence, employability and future-skills frameworks. It is designed to support reflection, pathway planning and practical capability development.

What the assessment measures

The assessment uses five capability domains. Each domain is scored from the answers you give in the tool. The final score is a weighted composite, so the result reflects practical AI use, human judgement and readiness to turn AI into useful work.

AI interactionHow often and how usefully you work with AI tools, including instruction, review and confidence.
Human advantageThe judgement, communication, lived context and transferable strengths you bring to AI-supported work.
AdaptabilityYour ability to keep learning, adjust when tools change and use feedback to improve.
Workflow thinkingYour ability to turn AI use into a repeatable process rather than a one-off experiment.
ReadinessYour practical readiness to test, measure and improve one useful AI-supported activity.

Overall scoring model

Most questions use a five-point scale. Answered questions convert into a 0–100 domain score. Unanswered areas stay low, which prevents the report from overstating capability when there is not enough evidence.

Final capability score

AI interaction × 1.20 + human advantage × 1.15 + adaptability × 1.00 + workflow thinking × 1.10 + readiness × 1.10, divided by 5.55.

Capability stages

The final score maps to one of five stages: Emerging, Exploring, Applied, Integrated or Strategic.

Capability stages and badges

The badges are a visual shorthand for the score band. They do not replace the detailed report; they help people recognise the stage quickly.

0–34
EmergingEarly familiarity and confidence-building.
35–54
ExploringExperimentation and early practical use.
55–69
AppliedUseful practical use with growing structure.
70–84
IntegratedAI is embedded into useful routines.
85–100
StrategicStrong practical judgement, implementation maturity and the ability to guide others.

How the three report signals are estimated

Human advantage

Estimated from the human-domain answers plus a capped strengths boost. The boost uses both strength depth and category breadth, with breadth carrying more weight, so a wide spread of strengths across categories is valued more than selecting many similar strengths.

Future-proofing signal

Adaptability × 35%, human advantage × 25%, workflow × 15%, category breadth × 15%, transferable strength mix × 10%.

Readiness signal

Action readiness × 45%, workflow clarity × 25%, adaptability × 20%, evidence from readiness answers × 10%.

Recommended pathway

The tool scores the main pathway options using the domain pattern, then selects the strongest fit. This means someone with a strong workflow signal can receive a workflow pathway, while someone with a strong human-and-AI support signal can receive a facilitator pathway.

These scores are indicative. They are designed to help a person see patterns, choose a practical next step and collect evidence of progress. They are not a formal competence certification.

What each report graphic means

These graphics are visual summaries of the same scoring model. They are included to make the report easier to read, but the numbers always come from the assessment answers and strength selections.

Capability score ringShows the final weighted score and the matching stage badge. The score combines AI interaction, human advantage, adaptability, workflow thinking and readiness.
Capability dashboardShows the five domain scores side by side, so users can see whether the strongest signal is tool use, human judgement, adaptability, workflow or readiness.
Human advantage signalShows the role of judgement, lived context, communication and transferable strengths. It uses human-domain answers, strength depth, category breadth and adaptability support.
Future-proofing signalShows how resilient the user’s capability mix looks as tools change. It combines adaptability, human advantage, workflow thinking, category breadth and transferable strength mix.
Readiness signalShows how close the user is to testing one useful AI-supported activity. It combines action readiness, workflow clarity, adaptability and evidence from readiness answers.
Recommended pathway lineShows the suggested route from assessment to pilot, refinement and application. The pathway is selected from the strongest fit across the user’s domain pattern.

Framework alignment

The frameworks below provide the reference language for digital competence, employability and future-facing learning. The Human-Driven AI model translates those ideas into a practical assessment for everyday AI use.

DigComp 2.2
  • Informs the AI interaction, information handling, review, safety and problem-solving parts of the assessment.
  • Supports the idea that digital competence includes judgement, communication, content creation and problem-solving, not just tool access.
Skills Builder Universal Framework
  • Informs the human strengths categories, especially communication, problem-solving, creativity, leadership and teamwork.
  • Supports the assessment’s focus on transferable capabilities that remain useful as tools change.
OECD Learning Compass 2030
  • Informs the future-proofing and readiness elements through agency, reflection, anticipation, action and lifelong learning.
  • Supports the HDAI emphasis on people using AI with purpose, context and responsibility.

Reference links

These links are included so anyone reviewing the methodology can check the source frameworks directly.

DigComp 2.2: The Digital Competence Framework for CitizensEuropean Commission Joint Research Centre publication. Skills Builder Universal FrameworkEssential skills framework used across education, employability and work. OECD Learning Compass 2030Learning framework focused on agency, transformation and future-ready capabilities.
Human-Driven AI uses these frameworks as reference points. The scoring model itself is an applied HDAI model created for reflection, pathway planning and capacity development.
© Human-Driven AI CIC, 2026. Indicative assessment for reflection, pathway planning and capacity development. This assessment runs in your browser. Your answers are not submitted or stored by Human-Driven AI unless you choose to share them.
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