B.3 Testing

1.1 Apply predefined test procedures to AI and ICT systems using guided examples and structured exercises.

Assessed by Review of AI test execution report, assessed for correct execution of predefined test procedures on AI and ICT systems using guided examples and structured exercises.

1.2 Identify basic errors, anomalies, or inconsistencies in AI model outputs in controlled datasets or simulated scenarios.

Assessed by Evaluation of AI error/anomaly log, assessed for accurate identification and description of basic errors, anomalies, or inconsistencies in AI model outputs from controlled datasets or simulated scenarios.

1.3 Recognize simple biases in AI outputs and flag them for further review by comparing outputs against reference standards or simple fairness checks.

Assessed by Assessment of AI bias report, assessed for recognition and flagging of simple biases in AI outputs through comparison with reference standards or simple fairness checks.

1.4 Document testing activities, results, and observations following established templates and reporting formats.

Assessed by Inspection of AI test documentation, assessed for complete and accurate recording of testing activities, results, and observations using established templates and reporting formats.

1.5 Recall relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

Assessed by Review of AI compliance checklist, assessed for correct identification of relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

2.1 Apply predefined test procedures to AI and ICT systems using guided examples and structured exercises.

Assessed by Review of AI test execution report, assessed for correct execution of predefined test procedures on AI and ICT systems using guided examples and structured exercises.

2.2 Identify basic errors, anomalies, or inconsistencies in AI model outputs in controlled datasets or simulated scenarios.

Assessed by Evaluation of AI error/anomaly log, assessed for accurate identification and description of basic errors, anomalies, or inconsistencies in AI model outputs from controlled datasets or simulated scenarios.

2.3 Recognize simple biases in AI outputs and flag them for further review by comparing outputs against reference standards or simple fairness checks.

Assessed by Assessment of AI bias report, assessed for recognition and flagging of simple biases in AI outputs through comparison with reference standards or simple fairness checks.

2.4 Document testing activities, results, and observations following established templates and reporting formats.

Assessed by Inspection of AI test documentation, assessed for complete and accurate recording of testing activities, results, and observations using established templates and reporting formats.

2.5 Recall relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

Assessed by Review of AI compliance checklist, assessed for correct identification of relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

4.1 Apply predefined test procedures to AI and ICT systems using guided examples and structured exercises.

Assessed by Review of AI test execution report, assessed for correct execution of predefined test procedures on AI and ICT systems using guided examples and structured exercises.

4.2 Identify basic errors, anomalies, or inconsistencies in AI model outputs in controlled datasets or simulated scenarios.

Assessed by Evaluation of AI error/anomaly log, assessed for accurate identification and description of basic errors, anomalies, or inconsistencies in AI model outputs from controlled datasets or simulated scenarios.

4.3 Recognize simple biases in AI outputs and flag them for further review by comparing outputs against reference standards or simple fairness checks.

Assessed by Assessment of AI bias report, assessed for recognition and flagging of simple biases in AI outputs through comparison with reference standards or simple fairness checks.

4.4 Document testing activities, results, and observations following established templates and reporting formats.

Assessed by Inspection of AI test documentation, assessed for complete and accurate recording of testing activities, results, and observations using established templates and reporting formats.

4.5 Recall relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

Assessed by Review of AI compliance checklist, assessed for correct identification of relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

2.1 Apply predefined test procedures to AI and ICT systems using guided examples and structured exercises.

Assessed by Review of AI test execution report, assessed for correct execution of predefined test procedures on AI and ICT systems using guided examples and structured exercises.

2.2 Identify basic errors, anomalies, or inconsistencies in AI model outputs in controlled datasets or simulated scenarios.

Assessed by Evaluation of AI error/anomaly log, assessed for accurate identification and description of basic errors, anomalies, or inconsistencies in AI model outputs from controlled datasets or simulated scenarios.

2.3 Recognize simple biases in AI outputs and flag them for further review by comparing outputs against reference standards or simple fairness checks.

Assessed by Assessment of AI bias report, assessed for recognition and flagging of simple biases in AI outputs through comparison with reference standards or simple fairness checks.

2.4 Document testing activities, results, and observations following established templates and reporting formats.

Assessed by Inspection of AI test documentation, assessed for complete and accurate recording of testing activities, results, and observations using established templates and reporting formats.

2.5 Recall relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

Assessed by Review of AI compliance checklist, assessed for correct identification of relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

2.1 Apply predefined test procedures to AI and ICT systems using guided examples and structured exercises.

Assessed by Guided AI/ICT test procedure execution assignment, assessed through completed test scripts, structured exercise outputs, and evidence that predefined test steps were followed correctly.

2.2 Identify basic errors, anomalies, or inconsistencies in AI model outputs in controlled datasets or simulated scenarios.

Assessed by AI output error and anomaly detection worksheet, assessed using controlled datasets or simulated scenarios for correct identification of basic errors, anomalies, and inconsistencies.

2.3 Recognize simple biases in AI outputs and flag them for further review by comparing outputs against reference standards or simple fairness checks.

Assessed by Basic AI bias and fairness check report, assessed for comparison of AI outputs against reference standards or simple fairness checks and correct flagging of potential bias for further review.

2.4 Document testing activities, results, and observations following established templates and reporting formats.

Assessed by Structured AI testing log and results report, assessed for complete documentation of test activities, results, observations, and use of established reporting templates.

2.5 Recall relevant ethical and regulatory standards applicable to AI testing in academic or guided professional contexts.

Assessed by Short written test or oral questioning on ethical and regulatory standards for AI testing, assessed for correct recall and basic application to guided AI testing scenarios

1.1 Apply evaluation procedures to test AI systems in partially unpredictable scenarios.

Assessed by Review of AI evaluation plan, assessed for appropriate application of evaluation procedures to test AI systems in partially unpredictable scenarios.

1.2 Analyse AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

Assessed by Assessment of AI analytical report, assessed for analysis of AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

1.3 Evaluate AI outputs for fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

Assessed by Evaluation of AI fairness/compliance report, assessed for evaluation of AI outputs against fairness, bias, ethical, and regulatory compliance criteria in practical system deployments.

1.4 Report findings with actionable recommendations for system improvement through structured reports or team presentations.

Assessed by Review of AI recommendation report, assessed for clear reporting of findings and actionable recommendations for system improvement.

1.5 Apply multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

Assessed by Inspection of AI metric/explainability outputs, assessed for correct application of multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis.

5.1 Apply evaluation procedures to test AI systems in partially unpredictable scenarios.

Assessed by Review of AI evaluation plan, assessed for appropriate application of evaluation procedures to test AI systems in partially unpredictable scenarios.

5.2 Analyse AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

Assessed by Assessment of AI analytical report, assessed for analysis of AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

5.3 Evaluate AI outputs for fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

Assessed by Evaluation of AI fairness/compliance report, assessed for evaluation of AI outputs against fairness, bias, ethical, and regulatory compliance criteria in practical system deployments.

5.4 Report findings with actionable recommendations for system improvement through structured reports or team presentations.

Assessed by Review of AI recommendation report, assessed for clear reporting of findings and actionable recommendations for system improvement.

5.5 Apply multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

Assessed by Inspection of AI metric/explainability outputs, assessed for correct application of multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis.

2.1 Apply evaluation procedures to test AI systems in partially unpredictable scenarios.

Assessed by Review of AI evaluation plan, assessed for appropriate application of evaluation procedures to test AI systems in partially unpredictable scenarios.

2.2 Analyse AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

Assessed by Assessment of AI analytical report, assessed for analysis of AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

2.3 Evaluate AI outputs for fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

Assessed by Evaluation of AI fairness/compliance report, assessed for evaluation of AI outputs against fairness, bias, ethical, and regulatory compliance criteria in practical system deployments.

2.4 Report findings with actionable recommendations for system improvement through structured reports or team presentations.

Assessed by Review of AI recommendation report, assessed for clear reporting of findings and actionable recommendations for system improvement.

2.5 Apply multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

Assessed by Inspection of AI metric/explainability outputs, assessed for correct application of multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis.

3.1 Apply evaluation procedures to test AI systems in partially unpredictable scenarios.

Assessed by Review of AI evaluation plan assessing application of evaluation procedures for testing AI systems in partially unpredictable scenarios.

3.2 Analyse AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

Assessed by Assessment of AI analytical report evaluating analysis of AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

3.3 Evaluate AI outputs for fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

Assessed by Evaluation of AI fairness/compliance report assessing fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

3.4 Report findings with actionable recommendations for system improvement through structured reports or team presentations.

Assessed by Review of AI recommendation report assessing structured findings and actionable recommendations for AI system improvement.

3.5 Apply multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

Assessed by Inspection of AI metric/explainability outputs assessing application of multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

4.1 Apply evaluation procedures to test AI systems in partially unpredictable scenarios.

Assessed by Review of AI evaluation plan assessing application of evaluation procedures for testing AI systems in partially unpredictable scenarios.

4.2 Analyse AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

Assessed by Assessment of AI analytical report evaluating analysis of AI model outputs for correctness, performance, robustness, and interpretability using quantitative metrics and exploratory techniques.

4.3 Evaluate AI outputs for fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

Assessed by Evaluation of AI fairness/compliance report assessing fairness, bias, and compliance with ethical and regulatory standards in practical system deployments.

4.4 Report findings with actionable recommendations for system improvement through structured reports or team presentations.

Assessed by Review of AI recommendation report assessing structured findings and actionable recommendations for AI system improvement.

4.5 Apply multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

Assessed by Inspection of AI metric/explainability outputs assessing application of multiple AI evaluation metrics and explainability techniques such as SHAP, LIME, or model confidence analysis to assess AI system behaviour.

3.1 Lead the design and implementation of AI and ICT testing frameworks in multidisciplinary project environments.

Assessed by Evaluation of an AI testing framework design and implementation plan, including definition of testing strategy, test levels (unit, integration, system), validation criteria, roles and responsibilities in a multidisciplinary team, and integration into the AI lifecycle; assessed for completeness, coherence, feasibility, and justification of design decisions.

3.2 Critically evaluate complex AI models for robustness, fairness, interpretability, explainability, and compliance using advanced evaluation tools and domain-specific criteria.

Assessed by Evaluation of a comprehensive AI model evaluation report, in which the learner applies advanced evaluation tools to assess a complex AI model across robustness, fairness, interpretability, explainability, and regulatory compliance. The report must include metric selection, benchmarking results, trade-off analysis, and domain-specific justification of evaluation criteria; assessed for methodological rigour, completeness, critical analysis, and validity of conclusions.

3.3 Innovate AI testing methodologies to improve model evaluation, validation, and auditing practices by integrating research findings or new algorithmic approaches.

Assessed by Evaluation of an AI testing methodology innovation report and prototype, in which the learner designs or adapts a testing methodology by integrating recent research findings or novel algorithmic approaches. The submission must include (1) a literature-informed rationale, (2) description of the proposed methodological innovation, (3) implementation or simulation of the approach, and (4) comparative evaluation demonstrating improvement over baseline testing practices; assessed for originality, methodological rigour, validity of improvement claims, and relevance to model evaluation, validation, and auditing.

3.4 Ensure ethical, legal, and organisational accountability in AI testing processes through governance frameworks and compliance audits.

Assessed by Assessment of an AI ethics and bias evaluation report, including identification of potential biases and fairness issues; evaluated for completeness, methodological soundness, and alignment with ethical and regulatory standards.

3.5 Coordinate testing activities across teams and integrate AI evaluation with overall system assessment in collaborative or cross-functional projects.

Assessed by Assessment of data visualisations and test result dashboards, evaluated for clarity, correctness, appropriateness of visual techniques, and effectiveness in communicating testing outcomes to stakeholders.

2.1 Lead the design and implementation of AI and ICT testing frameworks in multidisciplinary project environments.

Assessed by Review of AI testing framework documentation, assessed for leadership in the design and implementation of AI and ICT testing frameworks in multidisciplinary project environments.

2.2 Critically evaluate complex AI models for robustness, fairness, interpretability, explainability, and compliance using advanced evaluation tools and domain-specific criteria.

Assessed by Evaluation of AI critical evaluation report, assessed for critical evaluation of robustness, fairness, interpretability, explainability, and compliance using advanced evaluation tools and domain-specific criteria.

2.3 Innovate AI testing methodologies to improve model evaluation, validation, and auditing practices by integrating research findings or new algorithmic approaches.

Assessed by Assessment of AI methodology innovation report, assessed for innovation in AI testing methodologies and integration of research findings or new algorithmic approaches to improve evaluation, validation, and auditing practices.

2.4 Ensure ethical, legal, and organisational accountability in AI testing processes through governance frameworks and compliance audits.

Assessed by Review of AI ethics/compliance record, assessed for evidence of ethical, legal, and organizational accountability through governance frameworks and compliance audits in AI testing processes.

2.5 Coordinate testing activities across teams and integrate AI evaluation with overall system assessment in collaborative or cross-functional projects.

Assessed by Evaluation of AI team coordination report, assessed for coordination of testing activities across teams and integration of AI evaluation with overall system assessment in collaborative or cross-functional projects.