A.9 Innovating

3.1 Identify opportunities where AI can enhance existing products, services, or processes, using basic analysis of local business, societal, or research contexts.

Assessed by AI Opportunity Report, written assignment, and presentation of AI opportunity findings, assessed for identification of opportunities where AI can enhance existing products, services, or processes using basic analysis of local business, societal, or research contexts.

3.2 Develop functional and creative AI solutions in defined contexts, by applying established AI tools or frameworks.

Assessed by AI Prototype demonstration and code or model submission, assessed for development of functional and creative AI solutions in defined contexts using established AI tools or frameworks.

3.3 Adapt multiple technological concepts to implement AI-enabled solutions, such as combining data processing, models, and digital platforms.

Assessed by Technical documentation for AI implementation and portfolio entry, assessed for adaptation and combination of multiple technological concepts such as data processing, models, and digital platforms.

3.4 Evaluate the effectiveness of AI-based solutions in addressing local business, societal, or research challenges, using defined criteria or feedback.

Assessed by Evaluation report of AI solution with peer or tutor review, assessed for use of defined criteria or feedback to evaluate effectiveness in addressing local business, societal, or research challenges.

3.5 Explain design choices and limitations of AI-enabled solutions, using basic AI concepts, assumptions, or constraints.

Assessed by Design brief for AI solution and written justification of design choices, assessed for explanation of design choices and limitations using basic AI concepts, assumptions, or constraints.

3.6 Iterate and refine AI-based solutions based on testing or feedback, by making incremental improvements within given requirements.

Assessed by Iteration log or versioned AI prototype with reflection note on improvements, assessed for incremental refinements based on testing or feedback within given requirements.

2.1 Coordinate team efforts to design AI-enabled solutions addressing complex professional or societal problems, by aligning roles, expertise, and development activities.

Assessed by AI innovation opportunity identification report, where the learner, working in a group, identifies and articulates innovation opportunities using AI technologies within a defined domain or organisational context, and shows the team collaboration efforts. Assessed for originality, relevance, and alignment with technological and business trends.

2.2 Integrate multiple technical and organisational factors in developing AI-based solutions, such as system architecture, workflows, and governance constraints.

Assessed by Concept design document, including problem definition, proposed AI-enabled solution, and conceptual architecture, showing the integration of technical and organizational factors. Assessed for feasibility, coherence, and justification of design choices.

2.3 Apply AI technologies to implement solutions with practical impact, in real-world organisational or professional environments.

Assessed by Prototype or Proof-of-Concept (PoC) demonstrating the feasibility of the proposed AI innovation, accompanied by technical documentation. Assessed on functionality, appropriateness of approach, and ability to demonstrate key innovation aspects.

2.4 Evaluate the effectiveness and applicability of AI innovations in real-world professional contexts, using operational, stakeholder, or performance criteria.

Assessed by Comparative innovation analysis report, evaluating alternative AI solution approaches (e.g., models, architectures, or methodologies) with explicit trade-offs (e.g., performance, scalability, ethics). Assessed for analytical depth and quality of evaluation criteria.

2.5 Manage trade-offs between innovation, feasibility, and organisational constraints in AI projects, such as time, resources, and risk.

Assessed by Innovation impact & risk assessment report, analysing potential organisational, societal, ethical, and regulatory implications of the proposed AI solution. Assessed for completeness, critical reflection, and alignment with responsible AI principles.

2.6 Facilitate stakeholder engagement around AI-enabled innovations, by communicating value, limitations, and implementation implications.

Assessed by Innovation proposal pitch and defence, including a structured presentation to stakeholders (technical and non-technical), followed by Q&A. Assessed for persuasiveness, clarity, evidence-based argumentation, and ability to respond to critique and justify innovation decisions.

3.1 Coordinate team efforts to design AI-enabled solutions addressing complex professional or societal problems, by aligning roles, expertise, and development activities.

Assessed by Team collaboration report documenting AI solution design efforts, assessed for coordination of roles, expertise, and development activities to address complex professional or societal problems.

3.2 Integrate multiple technical and organisational factors in developing AI-based solutions, such as system architecture, workflows, and governance constraints.

Assessed by Architecture/integration report for AI solution, assessed for integration of system architecture, workflows, and governance constraints in the AI-based solution.

3.3 Apply AI technologies to implement solutions with practical impact, in real-world organisational or professional environments.

Assessed by Deployed AI solution demonstration or portfolio submission assessing practical implementation of AI technologies and evidence of impact in real-world organizational or professional environments.

3.4 Evaluate the effectiveness and applicability of AI innovations in real-world professional contexts, using operational, stakeholder, or performance criteria.

Assessed by Evaluation report measuring real-world AI solution effectiveness and applicability using operational, stakeholder, or performance criteria.

3.5 Manage trade-offs between innovation, feasibility, and organisational constraints in AI projects, such as time, resources, and risk.

Assessed by Risk/trade-off analysis report for AI innovation projects, assessed for management of trade- offs between innovation, feasibility, time, resources, and risk.

3.6 Facilitate stakeholder engagement around AI-enabled innovations, by communicating value, limitations, and implementation implications.

Assessed by Stakeholder communication materials (presentations, briefs) for AI innovations, assessed for communication of value, limitations, and implementation implications to stakeholders.

4.1 Coordinate team efforts to design AI-enabled solutions addressing complex professional or societal problems, by aligning roles, expertise, and development activities.

Assessed by Team collaboration report documenting AI solution design efforts, assessed for coordination of roles, expertise, and development activities to address complex professional or societal problems.

4.2 Integrate multiple technical and organisational factors in developing AI-based solutions, such as system architecture, workflows, and governance constraints.

Assessed by Architecture/integration report for AI solution, assessed for integration of system architecture, workflows, and governance constraints in the AI-based solution.

4.3 Apply AI technologies to implement solutions with practical impact, in real-world organisational or professional environments.

Assessed by Deployed AI solution demonstration or portfolio submission assessing practical implementation of AI technologies and evidence of impact in real-world organizational or professional environments.

4.4 Evaluate the effectiveness and applicability of AI innovations in real-world professional contexts, using operational, stakeholder, or performance criteria.

Assessed by Evaluation report measuring real-world AI solution effectiveness and applicability using operational, stakeholder, or performance criteria.

4.5 Manage trade-offs between innovation, feasibility, and organisational constraints in AI projects, such as time, resources, and risk.

Assessed by Risk/trade-off analysis report for AI innovation projects, assessed for management of trade- offs between innovation, feasibility, time, resources, and risk.

4.6 Facilitate stakeholder engagement around AI-enabled innovations, by communicating value, limitations, and implementation implications.

Assessed by Stakeholder communication materials, including presentations or briefs for AI innovations, assessed for communication of value, limitations, and implementation implications to stakeholders.

2.1 Define and justify strategic directions and priorities for AI-driven innovation in complex organisational or societal contexts.

Assessed by Assessment of an AI strategic priority framework and AI contextual analysis report, supported by a written strategic justification, assessed for definition and justification of AI- driven innovation priorities in complex organizational or societal contexts.

2.2 Envision and conceptualize transformative AI applications that create new markets, services, organisational capabilities, or societal value.

Assessed by Evaluation of a transformative AI application concept dossier and AI market or ecosystem model, assessed through a vision paper review for creation of new markets, services, organizational capabilities, or societal value.

2.3 Lead and influence multidisciplinary teams and stakeholders in the realization of AI-enabled innovation initiatives at organisational or ecosystem level.

Assessed by Assessment of an AI innovation governance model and AI stakeholder strategy, supported by documented AI leadership decisions and a reflective leadership narrative, assessed for leadership and influence across multidisciplinary teams and stakeholders at organizational or ecosystem level.

2.4 Select, adapt, and integrate advanced AI methods and architectures to maximize innovation impact, scalability, and sustainability.

Assessed by Technical assessment of an advanced AI architecture document and AI method selection justification, including review of an AI prototype or proof-of-concept, assessed for selection, adaptation, and integration of advanced AI methods and architectures to maximize innovation impact, scalability, and sustainability.

2.5 Translate advanced AI capabilities into viable innovation pathways by aligning technical potential with strategic, operational, and business considerations.

Assessed by Evaluation of an AI-enabled business or operating model and AI scalability and feasibility analysis, assessed through a written innovation case for alignment of advanced AI capabilities with strategic, operational, and business considerations.

2.6 Critically evaluate the outcomes and impacts of AI-driven innovations using qualitative and quantitative approaches.

Assessed by Assessment of an AI innovation evaluation framework and AI impact assessment report, supported by evidence-based performance analysis, assessed for critical evaluation of AI- driven innovation outcomes and impacts using qualitative and quantitative approaches.

2.7 Anticipate, assess, and govern ethical, legal, and societal implications of AI-driven innovation, embedding responsible AI principles into strategic decision-making.

Assessed by Evaluation of a responsible AI governance framework and AI ethical, legal, and risk assessment, assessed through a policy or governance review for anticipation, assessment, and governance of ethical, legal, and societal implications in strategic AI decision-making.

2.8 Generate practice-based knowledge and original insights that advance professional practice in AI-driven innovation through rigorous inquiry and critical reflection.

Assessed by Examination of a doctoral-level AI practice-based research report and original AI innovation contribution, including defence or viva, assessed for rigorous inquiry, critical reflection, and advancement of professional practice in AI-driven innovation.

2.9 Reflect on and adapt AI innovation strategies in response to uncertainty, emerging evidence, and evolving technological or societal conditions.

Assessed by Assessment of an AI reflective strategy portfolio and revised AI innovation strategy, evaluated through critical reflection and adaptive reasoning in response to uncertainty, emerging evidence, and evolving technological or societal conditions.

6.1 Define and justify strategic directions and priorities for AI-driven innovation in complex organisational or societal contexts.

Assessed by Assessment of an AI strategic priority framework and AI contextual analysis report, supported by a written strategic justification, evaluated for strategic direction, prioritisation, and fit with complex organizational or societal contexts.

6.2 Envision and conceptualize transformative AI applications that create new markets, services, organisational capabilities, or societal value.

Assessed by Evaluation of a transformative AI application concept dossier and AI market or ecosystem model, assessed through a vision paper review for potential to create new markets, services, organizational capabilities, or societal value.

6.3 Lead and influence multidisciplinary teams and stakeholders in the realization of AI-enabled innovation initiatives at organisational or ecosystem level.

Assessed by Assessment of an AI innovation governance model and AI stakeholder strategy, supported by documented AI leadership decisions and a reflective leadership narrative, evaluated for leadership and influence across multidisciplinary teams and stakeholders.

6.4 Select, adapt, and integrate advanced AI methods and architectures to maximize innovation impact, scalability, and sustainability.

Assessed by Technical assessment of an advanced AI architecture document and AI method selection justification, including review of an AI prototype or proof-of-concept, evaluated for selection, adaptation, and integration of AI methods and architectures.

6.5 Translate advanced AI capabilities into viable innovation pathways by aligning technical potential with strategic, operational, and business considerations.

Assessed by Evaluation of an AI-enabled business or operating model and AI scalability and feasibility analysis, assessed through a written innovation case for alignment of technical potential with strategic, operational, and business considerations.

6.6 Critically evaluate the outcomes and impacts of AI-driven innovations using qualitative and quantitative approaches.

Assessed by Assessment of an AI innovation evaluation framework and AI impact assessment report, supported by evidence-based performance analysis using qualitative and quantitative approaches.

6.7 Anticipate, assess, and govern ethical, legal, and societal implications of AI-driven innovation, embedding responsible AI principles into strategic decision-making.

Assessed by Evaluation of a responsible AI governance framework and AI ethical, legal, and risk assessment, assessed through a policy or governance review for responsible AI integration into strategic decision-making.

6.8 Generate practice-based knowledge and original insights that advance professional practice in AI-driven innovation through rigorous inquiry and critical reflection.

Assessed by Examination of a doctoral-level AI practice-based research report and original AI innovation contribution, including defence or viva, assessed for rigorous inquiry, critical reflection, and advancement of professional practice.

6.9 Reflect on and adapt AI innovation strategies in response to uncertainty, emerging evidence, and evolving technological or societal conditions.

Assessed by Assessment of an AI reflective strategy portfolio and revised AI innovation strategy, evaluated for critical reflection, adaptive reasoning, and response to uncertainty, emerging evidence, and evolving technological or societal conditions.

5.1 Define and justify strategic directions and priorities for AI-driven innovation in complex organisational or societal contexts.

Assessed by Assessment of an AI strategic priority framework and AI contextual analysis report, supported by a written strategic justification, evaluated for strategic direction, prioritisation, and fit with complex organizational or societal contexts.

5.2 Envision and conceptualize transformative AI applications that create new markets, services, organisational capabilities, or societal value.

Assessed by Evaluation of a transformative AI application concept dossier and AI market or ecosystem model, assessed through a vision paper review for potential to create new markets, services, organizational capabilities, or societal value.

5.3 Lead and influence multidisciplinary teams and stakeholders in the realization of AI-enabled innovation initiatives at organisational or ecosystem level.

Assessed by Assessment of an AI innovation governance model and AI stakeholder strategy, supported by documented AI leadership decisions and a reflective leadership narrative, evaluated for leadership and influence across multidisciplinary teams and stakeholders.

5.4 Select, adapt, and integrate advanced AI methods and architectures to maximize innovation impact, scalability, and sustainability.

Assessed by Technical assessment of an advanced AI architecture document and AI method selection justification, including review of an AI prototype or proof-of-concept, evaluated for selection, adaptation, and integration of AI methods and architectures.

5.5 Translate advanced AI capabilities into viable innovation pathways by aligning technical potential with strategic, operational, and business considerations.

Assessed by Evaluation of an AI-enabled business or operating model and AI scalability and feasibility analysis, assessed through a written innovation case for alignment of technical potential with strategic, operational, and business considerations.

5.6 Critically evaluate the outcomes and impacts of AI-driven innovations using qualitative and quantitative approaches.

Assessed by Assessment of an AI innovation evaluation framework and AI impact assessment report, supported by evidence-based performance analysis using qualitative and quantitative approaches.

5.7 Anticipate, assess, and govern ethical, legal, and societal implications of AI-driven innovation, embedding responsible AI principles into strategic decision-making.

Assessed by Evaluation of a responsible AI governance framework and AI ethical, legal, and risk assessment, assessed through a policy or governance review for responsible AI integration into strategic decision-making.

5.8 Generate practice-based knowledge and original insights that advance professional practice in AI-driven innovation through rigorous inquiry and critical reflection.

Assessed by Examination of an AI science- or practice-based research report and original AI innovation contribution, including presentation and defence, assessed for rigorous inquiry, critical reflection, and advancement of professional practice.

5.9 Reflect on and adapt AI innovation strategies in response to uncertainty, emerging evidence, and evolving technological or societal conditions.

Assessed by AI Reflective Strategy Portfolio and Revised AI Innovation Strategy, assessed for critical reflection, adaptive reasoning, and evidence-based adjustment of AI innovation strategies in response to uncertainty, emerging evidence, and evolving technological or societal conditions.