Artificial-Intelligence Awareness (AI-A)

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Artificial-Intelligence Awareness (AI-A)

1 | Purpose and Societal Need

Modern workplaces bring everyone—engineers, decision-makers, legal teams, product managers, even end-users—into contact with AI systems. This exam objectively measures whether a candidate understands core AI concepts, limitations, ethical principles, and regulatory frameworks.

  • Technological Literacy Builds a grasp of basic algorithm types, data requirements, and model failure modes.

  • Responsible Use Raises awareness of bias, fairness, and privacy risks.

  • Compliance & Governance Links practice to the EU AI Act, OECD AI Principles, GDPR / KVKK, and similar regulations—laying the groundwork for effective internal governance.

The result is a shared language and sense of responsibility across every role that touches AI projects.

2 | Exam Architecture

Feature Detail
Number of Questions 50 multiple-choice (single correct)
Duration 120 min (≈ 2 min per question)
Pass Mark 50 %
Delivery Online, remotely proctored, instant scoring
Credential Web + QR verification
Retakes One free retake within 12 months

Psychometric analysis produced Cronbach’s α = 0.81, indicating high reliability.

3 | Assessed Competency Domains

(sample stems drawn from the uploaded question bank)

Domain Example Stem
AI Definitions & History “What distinguishes machine learning from rule-based expert systems?”
Algorithm Fundamentals “Which data-labelling approach differentiates supervised from unsupervised learning?”
Data Quality & Pre-Processing “What is the first strategy to consider when faced with a high percentage of missing values?”
Ethics & Fairness “Which concept measures the impact of algorithmic bias on those affected?”
Privacy & Security “What term describes the risk of extracting training data from model outputs?”
Regulation & Standards “According to the EU AI Act, what is the shared responsibility for ‘high-risk’ systems?”
AI Failure Modes “In text-generation models, what does hallucination refer to?”
Sustainability & Energy “Name one method for reducing the carbon footprint of model training.”

4 | Scientific and Organisational Significance

  • Shared Understanding Strengthens consistent terminology across multidisciplinary teams.

  • Risk Mitigation Helps surface faulty assumptions early in a project’s life-cycle.

  • Continuous Improvement The feedback report highlights knowledge gaps, enabling targeted follow-up training.

Note Because the exam measures awareness rather than deep technical skill, it is appropriate for all stakeholders involved in AI initiatives.

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What Will You Learn?

  • Understand core AI concepts, including definitions, key milestones, and application types
  • Differentiate between algorithm types (e.g., supervised vs. unsupervised learning)
  • Recognize the importance of data quality and basic pre-processing strategies
  • Identify ethical risks, including bias, fairness, and lack of transparency
  • Understand privacy and security concerns, such as data leakage and model inversion
  • Gain awareness of key AI regulations (EU AI Act, GDPR, KVKK, OECD AI Principles)
  • Recognize AI system failure modes, including overfitting, hallucination, and feedback loops
  • Develop awareness of sustainability concerns in AI (e.g., energy consumption and mitigation)
  • Build a shared understanding of governance frameworks within AI projects
  • Use the diagnostic score report to identify knowledge gaps and guide future training

Course Content

AI Awareness

  • AI Awareness

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