A.6 Application Design

2.1 analyse and translate user or project requirements into AI application specifications using structured requirement-gathering methods.

Assessed by Review of AI requirement specification document and oral questioning on requirement analysis, assessed for accurate translation of user or project requirements into AI application specifications using structured requirement-gathering methods.

2.2 design AI solutions for routine tasks, including data structures, workflows, and AI model selection in clearly defined project scenarios.

Assessed by Evaluation of AI design document, workflow diagrams, and design critique sessions, assessed for suitability of data structures, workflows, and AI model selection for routine tasks in clearly defined project scenarios.

2.3 select suitable AI methods, frameworks, and tools by evaluating project constraints and available resources.

Assessed by Assessment of AI technology selection report, including justification of AI methods, frameworks, and tools against project constraints and available resources.

2.4 demonstrate awareness of the interactions between AI applications and complex system environments such as enterprise or multi-component systems to ensure correct integration.

Assessed by Analysis of AI integration assessment report with peer or instructor review, assessed for demonstrated awareness of interactions between AI applications and enterprise or multi- component system environments.

2.5 apply user/customer needs and usability principles in prototype testing or iterative development cycles to ensure the AI solution is functional and user-aligned.

Assessed by Assessment of AI usability report, including demonstration of prototype and user feedback summary, assessed for application of user/customer needs and usability principles in prototype testing or iterative development cycles.

2.6 validate AI models and workflows through iterative testing and feedback using representative datasets or user scenarios, ensuring they meet intended outcomes.

Assessed by Review of AI validation report with test results, automated model evaluation, and instructor feedback, assessed for iterative testing and feedback using representative datasets or user scenarios and for evidence that intended outcomes are met.

1.1 Analyse and translate user or project requirements into AI application specifications using structured requirement-gathering methods.

Assessed by Review of AI requirement specification document and oral questioning on requirement analysis, assessed for accurate translation of user or project requirements into AI application specifications using structured requirement-gathering methods.

1.2 Design AI solutions for routine tasks, including data structures, workflows, and AI model selection in clearly defined project scenarios.

Assessed by Evaluation of AI design document, workflow diagrams, and design critique sessions, assessed for suitability of data structures, workflows, and AI model selection for routine tasks in clearly defined project scenarios.

1.3 Select suitable AI methods, frameworks, and tools by evaluating project constraints and available resources.

Assessed by Assessment of AI technology selection report, including justification of selected AI methods, frameworks, and tools against project constraints and available resources.

1.4 Demonstrate awareness of the interactions between AI applications and complex system environments such as enterprise or multi-component systems to ensure correct integration.

Assessed by Analysis of AI integration assessment report with peer or instructor review, assessed for demonstrated awareness of interactions between AI applications and enterprise or multi- component system environments.

1.5 Apply user/customer needs and usability principles in prototype testing or iterative development cycles to ensure the AI solution is functional and user-aligned.

Assessed by Assessment of AI usability report, including demonstration of prototype and user feedback summary, assessed for application of user/customer needs and usability principles in prototype testing or iterative development cycles.

1.6 Validate AI models and workflows through iterative testing and feedback using representative datasets or user scenarios, ensuring they meet intended outcomes.

Assessed by Review of AI validation report with test results, automated model evaluation, and instructor feedback, assessed for iterative testing and feedback using representative datasets or user scenarios and for evidence that intended outcomes are met.

1.1 Analyse and translate user or project requirements into AI application specifications using structured requirement-gathering methods.

Assessed by Review of AI requirement specification document and oral questioning on requirement analysis, assessed for accurate translation of user or project requirements into AI application specifications using structured requirement-gathering methods.

1.2 Design AI solutions for routine tasks, including data structures, workflows, and AI model selection in clearly defined project scenarios.

Assessed by Evaluation of AI design document, workflow diagrams, and design critique sessions, assessed for suitability of data structures, workflows, and AI model selection for routine tasks in clearly defined project scenarios.

1.3 Select suitable AI methods, frameworks, and tools by evaluating project constraints and available resources.

Assessed by Assessment of AI technology selection report, including justification of selected AI methods, frameworks, and tools against project constraints and available resources.

1.4 Demonstrate awareness of the interactions between AI applications and complex system environments such as enterprise or multi-component systems to ensure correct integration.

Assessed by Analysis of AI integration assessment report with peer or instructor review, assessed for demonstrated awareness of interactions between AI applications and enterprise or multi- component system environments.

1.5 Apply user/customer needs and usability principles in prototype testing or iterative development cycles to ensure the AI solution is functional and user-aligned.

Assessed by Assessment of AI usability report, including demonstration of prototype and user feedback summary, assessed for application of user/customer needs and usability principles in prototype testing or iterative development cycles.

1.6 Validate AI models and workflows through iterative testing and feedback using representative datasets or user scenarios, ensuring they meet intended outcomes.

Assessed by Review of AI validation report with test results, automated model evaluation, and instructor feedback, assessed for iterative testing and feedback using representative datasets or user scenarios and for evidence that intended outcomes are met.

1.1 analyse and translate user or project requirements into AI application specifications using structured requirement-gathering methods.

Assessed by Review of AI requirement specification document and oral questioning on requirement analysis, assessed for accurate translation of user or project requirements into AI application specifications using structured requirement-gathering methods.

1.2 design AI solutions for routine tasks, including data structures, workflows, and AI model selection in clearly defined project scenarios.

Assessed by Evaluation of AI design document and workflow diagrams, supported by design critique sessions, assessed for data structures, workflows, and AI model selection in clearly defined project scenarios.

1.3 select suitable AI methods, frameworks, and tools by evaluating project constraints and available resources.

Assessed by Assessment of AI technology selection report, including justification of AI methods, frameworks, and tools against project constraints and available resources.

1.4 demonstrate awareness of the interactions between AI applications and complex system environments such as enterprise or multi-component systems to ensure correct integration.

Assessed by Analysis of AI integration assessment report, with peer or instructor review, assessed for awareness of interactions between AI applications and enterprise or multi-component system environments.

1.5 apply user/customer needs and usability principles in prototype testing or iterative development cycles to ensure the AI solution is functional and user-aligned.

Assessed by Assessment of AI usability report, including prototype demonstration and user feedback summary, assessed for application of user/customer needs and usability principles in iterative development.

1.6 validate AI models and workflows through iterative testing and feedback using representative datasets or user scenarios, ensuring they meet intended outcomes.

Assessed by Review of AI validation report with test results, automated model evaluation, and instructor feedback, assessed for iterative validation of AI models and workflows using representative datasets or user scenarios.

2.1 Lead the conceptualization and architecture of complex AI applications in multi-stakeholder or cross-domain projects.

Assessed by Review of AI architecture specification and presentation of system design to stakeholders or instructors, assessed for leadership in conceptualizing and architecting complex AI applications in multi-stakeholder or cross-domain projects.

2.2 Design scalable and interoperable data structures, workflows, and AI models using advanced modelling and simulation techniques to ensure robustness and ethical compliance.

Assessed by Assessment of AI system design artifacts, including pipeline diagrams, models, and workflows, assessed for scalability, interoperability, robustness, ethical compliance, and use of advanced modelling or simulation techniques.

2.3 Evaluate design trade-offs and provide recommendations to stakeholders on AI methods, performance, cost, and ethical considerations through structured decision-making and scenario analysis.

Assessed by Evaluation of AI design trade-off report, oral defence, or stakeholder advisory simulation assessing structured recommendations on AI methods, performance, cost, and ethical considerations.

2.4 Integrate explainability, fairness, privacy, and security principles into AI system design by following responsible AI frameworks and guidelines.

Assessed by Review of AI ethical compliance documentation, including compliance checklist and reflection report, assessed for integration of explainability, fairness, privacy, and security principles into AI system design.

2.5 Oversee the integration of AI solutions into heterogeneous environments such as cloud- based, edge, or multi-platform systems, optimising system performance and resource use.

Assessed by Assessment of AI system integration report and demonstration of deployed AI components, assessed for integration into heterogeneous environments and optimization of system performance and resource use.

2.6 Lead iterative development and validation with representative users using feedback loops, benchmarking, or pilot deployments to ensure solution effectiveness, scalability, and alignment with project objectives.

Assessed by Review of AI evaluation report including benchmarking results, pilot feedback, and iterative improvements, assessed for user-informed validation, solution effectiveness, scalability, and alignment with project objectives.