D.3 Education and Training Provision

4.1 Design AI training modules that address organisational AI skill gaps and foster employee AI literacy, using role-specific AI use cases and practical learning scenarios.

Assessed by Assessment of AI training module design document and AI use-case mapping for coverage of organizational AI skill gaps, employee AI literacy, role-specific AI use cases, and practical learning scenarios.

4.2 Implement AI training programmes by delivering sessions and applying AI tools or simulations to illustrate AI concepts, in workplace-based learning and training environments.

Assessed by Evaluation of AI training materials and recorded or observed AI training session assessing delivery of training and use of AI tools or simulations to illustrate AI concepts in workplace- based learning environments.

4.3 Monitor and adjust AI learning programmes using feedback and performance data to improve team-level AI competence, by analysing learning analytics and training outcomes.

Assessed by Review of AI learning feedback summary and revised AI training content assessing use of feedback, performance data, learning analytics, and training outcomes to improve team-level AI competence.

4.4 Integrate AI ethics and governance principles into training content to ensure responsible AI adoption, such as fairness, transparency, and data protection in applied AI use.

Assessed by Assessment of AI ethics and governance training materials and applied AI case examples for integration of fairness, transparency, data protection, and responsible AI adoption principles.

4.5 Support learners during AI training activities by explaining concepts, facilitating discussions, and responding to questions, in guided and instructor-led training settings.

Assessed by Evaluation of documented AI learner support evidence and facilitation notes assessing explanation of concepts, facilitation of discussions, and responses to learner questions in guided or instructor-led training settings.

4.6 Apply basic instructional design principles to structure AI learning content and activities, using established training frameworks and learning objectives.

Assessed by Review of AI instructional design rationale and alignment with learning objectives, assessed for use of basic instructional design principles, established training frameworks, and coherent learning activities.

5.1 Design AI training modules that address organisational AI skill gaps and foster employee AI literacy, using role-specific AI use cases and practical learning scenarios.

Assessed by Assessment of AI training module design document and AI use-case mapping for coverage of organizational AI skill gaps, employee AI literacy, role-specific AI use cases, and practical learning scenarios.

5.2 Implement AI training programmes by delivering sessions and applying AI tools or simulations to illustrate AI concepts, in workplace-based learning and training environments.

Assessed by Evaluation of AI training materials and recorded or observed AI training session assessing delivery of training and use of AI tools or simulations to illustrate AI concepts in workplace- based learning environments.

5.3 Monitor and adjust AI learning programmes using feedback and performance data to improve team-level AI competence, by analysing learning analytics and training outcomes.

Assessed by Review of AI learning feedback summary and revised AI training content assessing use of feedback, performance data, learning analytics, and training outcomes to improve team-level AI competence.

5.4 Integrate AI ethics and governance principles into training content to ensure responsible AI adoption, such as fairness, transparency, and data protection in applied AI use.

Assessed by Assessment of AI ethics and governance training materials and applied AI case examples for integration of fairness, transparency, data protection, and responsible AI adoption principles.

5.5 Support learners during AI training activities by explaining concepts, facilitating discussions, and responding to questions, in guided and instructor-led training settings.

Assessed by Evaluation of documented AI learner support evidence and facilitation notes assessing explanation of concepts, facilitation of discussions, and responses to learner questions in guided or instructor-led training settings.

5.6 Apply basic instructional design principles to structure AI learning content and activities, using established training frameworks and learning objectives.

Assessed by Review of AI instructional design rationale and alignment with learning objectives, assessed for use of basic instructional design principles, established training frameworks, and coherent learning activities.

5.1 Design AI training modules that address organisational AI skill gaps and foster employee AI literacy, using role-specific AI use cases and practical learning scenarios.

Assessed by Assessment of AI training module design document and AI use-case mapping for coverage of organizational AI skill gaps, employee AI literacy, role-specific AI use cases, and practical learning scenarios.

5.2 Implement AI training programmes by delivering sessions and applying AI tools or simulations to illustrate AI concepts, in workplace-based learning and training environments.

Assessed by Evaluation of AI training materials and recorded or observed AI training session assessing delivery of training and use of AI tools or simulations to illustrate AI concepts in workplace- based learning environments.

5.3 Monitor and adjust AI learning programmes using feedback and performance data to improve team-level AI competence, by analysing learning analytics and training outcomes.

Assessed by Review of AI learning feedback summary and revised AI training content assessing use of feedback, performance data, learning analytics, and training outcomes to improve team-level AI competence.

5.4 Integrate AI ethics and governance principles into training content to ensure responsible AI adoption, such as fairness, transparency, and data protection in applied AI use.

Assessed by Assessment of AI ethics and governance training materials and applied AI case examples for integration of fairness, transparency, data protection, and responsible AI adoption principles.

5.5 Support learners during AI training activities by explaining concepts, facilitating discussions, and responding to questions, in guided and instructor-led training settings.

Assessed by Evaluation of documented AI learner support evidence and facilitation notes assessing explanation of concepts, facilitation of discussions, and responses to learner questions in guided or instructor-led training settings.

5.6 Apply basic instructional design principles to structure AI learning content and activities, using established training frameworks and learning objectives.

Assessed by Review of AI instructional design rationale and alignment with learning objectives, assessed for use of basic instructional design principles, established training frameworks, and coherent learning activities.

3.1 Develop enterprise-wide AI training strategies that enhance organisational AI capability and workforce AI literacy, by aligning learning initiatives with business strategy and AI maturity levels.

Assessed by Assessment of enterprise AI training and literacy strategy document for alignment with business strategy, AI maturity levels, organizational AI capability, and workforce AI literacy needs.

3.2 Coordinate cross-functional AI training initiatives across departments, aligning resources, objectives, and stakeholder needs, in complex organisational and multi-disciplinary contexts.

Assessed by Evaluation of cross-functional AI training coordination plan and stakeholder alignment evidence, assessed for alignment of resources, objectives, and stakeholder needs across departments.

3.3 Evaluate the effectiveness of AI competence programs using performance indicators, organisational impact data, and AI adoption metrics, such as productivity gains, capability growth, and responsible AI usage.

Assessed by Review of AI training impact evaluation report and AI competence metrics dashboard, assessed for use of performance indicators, organizational impact data, AI adoption metrics, and conclusions on programme effectiveness.

3.4 Plan and align AI learning initiatives with emerging AI technologies, governance frameworks, and ethical standards to drive strategic organisational goals, in response to evolving technological and regulatory developments.

Assessed by Assessment of AI governance- and ethics-aligned learning plan addressing emerging AI technologies, evaluated for alignment with strategic organizational goals and evolving technological and regulatory developments.

3.5 Design scalable AI competence development frameworks that support long-term organisational learning, across multiple roles, functions, and levels of AI expertise.

Assessed by Evaluation of scalable AI competence development framework and role-based AI capability mapping, assessed for coverage of multiple roles, functions, expertise levels, and long-term organizational learning.

3.6 Advise organisational leadership on AI training priorities and investments, using evidence from competence assessments, risk analyses, and strategic AI roadmaps.

Assessed by Review of executive AI training advisory brief supported by AI competence gap analysis, risk analysis, and strategic AI roadmap evidence, assessed for justified training priorities and investment recommendations.