AI Literacy Assessment (EU AI Act Art. 4)

Anonymous self-assessment of your employees' AI literacy – as a needs analysis and documentation basis for the training obligation under Article 4 of the EU AI Act.

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AI Literacy Assessment (EU AI Act Art. 4) – questionnaire preview

Since 2 February 2025, Article 4 of the EU AI Act obliges organisations deploying AI to ensure a sufficient level of AI literacy among their staff. The first step is an honest baseline: who uses AI how often, how confident are teams in applying and evaluating it and in handling its risks – and where is training most urgent? This template draws on established AI literacy scales such as the Meta AI Literacy Scale (MAILS, University of Würzburg) and covers three competence areas: applying & using, understanding & evaluating, and ethics & risks. The results double as a documented needs analysis for your AI Act compliance file.

When should you use this template?

This template is a great fit for:

  • As a needs analysis and documentation basis for the AI literacy obligation under Article 4 of the EU AI Act
  • Before and after AI training to make learning progress measurable
  • As a baseline per department when rolling out AI tools

Every question in this template

  1. 1

    I understand that participation is voluntary and anonymous, and I take part. *

    Consent
    Agreement required
  2. 2

    How often do you use AI tools (e.g. ChatGPT, Copilot, DeepL) for your work? *

    Single choice
    • Never
    • Less than monthly
    • Several times a month
    • Several times a week
    • Daily
  3. 3

    Applying & using

    How much do the following statements apply to you?

    Matrix
    Strongly disagreeDisagreeNeutralAgreeStrongly agree
    I can confidently use common AI tools for my tasks.
    I can phrase prompts so that I get useful results.
    I know which tasks in my daily work AI is well suited for – and which not.
    I can integrate AI output into my workflows in a meaningful way.
  4. 4

    Understanding & evaluating

    Matrix
    Strongly disagreeDisagreeNeutralAgreeStrongly agree
    I understand in broad terms how generative AI produces its answers.
    I can judge how reliable an AI result is.
    I recognise typical AI errors, e.g. fabricated facts (hallucinations).
    I verify AI output before using it further.
  5. 5

    Ethics & risks

    Matrix
    Strongly disagreeDisagreeNeutralAgreeStrongly agree
    I know which data I may enter into AI tools – and which not.
    I know our organisation's internal rules on AI use.
    I pay attention to copyright and confidentiality when working with AI.
    I am aware that AI output can contain bias.
  6. 6

    Overall, how would you rate your AI literacy?

    Single choice
    • Beginner – I have hardly used AI so far
    • Basic – I occasionally use AI for simple tasks
    • Advanced – AI is a regular part of how I work
    • Expert – I also help others use AI
  7. 7

    On which topics would you like training or support?

    Multiple answers possible.

    Multiple choice
    • Basics: how does generative AI work?
    • Better prompting: phrasing effective requests
    • Data protection, copyright and the EU AI Act
    • AI tools for my specific area of work
    • Verifying and evaluating AI output
    • No training needed
  8. 8

    Where could AI take the most work off your plate?

    Long text

From questionnaire to dashboard

Each question automatically becomes a variable in your DataLion dataset. From it you build a competence dashboard you can filter by department, location or wave:

  • AI literacy index: The mean across all competence statements as a KPI tile – your headline figure for AI Act reporting and before/after training comparisons.
  • Profile of the three competence areas: Mean-value bars for applying, understanding and ethics & risks show at a glance which area needs training first.
  • Agreement profile per statement: Diverging bars show agreement versus disagreement for every single statement – ideal for spotting concrete knowledge gaps.
  • Competence heatmap by department: Cross competence areas with departments: the heatmap shows where targeted training is needed instead of one-size-fits-all courses.
  • Requested training topics: The multiple-choice training wishes as a donut or bar chart – this becomes your training plan.
  • AI topic analysis of open answers: Analyse the open question on use-case ideas with DataLion's AI topic and sentiment analysis – as a word cloud or topic list.

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Frequently asked questions

What exactly does Article 4 of the EU AI Act require?
Since 2 February 2025, providers and deployers of AI systems must take measures to ensure, to their best extent, a sufficient level of AI literacy among their staff – taking prior knowledge and context of use into account. A documented needs analysis plus training built on it is the common way to evidence this. This template provides the needs analysis; it is not legal advice.
Is the questionnaire based on a validated instrument?
The items draw on established, freely available AI literacy scales, in particular the Meta AI Literacy Scale (MAILS) by Carolus et al. (University of Würzburg), shortened and simplified for workplace use. For academic studies we recommend the original scale.
Should the survey be anonymous?
Yes. The goal is honest self-assessment, not performance review. Only analyse results in groups of five or more. Aggregated results per unit are sufficient as training-needs evidence.

Start with this template

Load the template into DataLion, adapt it to your brand and start collecting responses — GDPR-compliant, in minutes.

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