A.10 User Experience

1.1 Analyse user needs, goals, and behaviour in AI-supported applications, such as recommendation or automation systems.

Assessed by AI user research report, submission and review, with practical observation of research artefacts; assessed for clarity of user needs analysis, relevance of evidence, and alignment with the data/AI use context.

1.2 Apply and adapt UX and HCI methods using AI design tools and frameworks to design user interactions for AI-enabled products and services.

Assessed by AI UX prototype evaluation, practical design assignment, rubric-based assessment; evaluated for usability, appropriateness of design choices, and alignment with user requirements and analytical objectives.

1.3 Design interface elements such as visual indicators or textual cues that communicate AI behaviour, limitations, or confidence levels to users, and evaluate usability, explainability, and trust using methods like user testing or heuristics.

Assessed by AI interface mock-ups and heuristic evaluation report, peer and instructor review; assessed for clarity, consistency, usability issues identified, and quality of improvement recommendations.

1.4 Assess ethical implications of AI-driven user experiences by considering issues such as bias, fairness, and transparency.

Assessed by AI ethics assessment report, written case analysis, and ethical decision reflection; assessed for identification of ethical risks, quality of reasoning, and relevance to responsible AI- supported data analysis.

5.1 Analyse user needs, goals, and behaviour in AI-supported applications, such as recommendation or automation systems.

Assessed by AI user research report, submission and review, and practical observation of research artefacts assessing analysis of user needs, goals, and behaviour in AI-supported applications.

5.2 Apply and adapt UX and HCI methods using AI design tools and frameworks to design user interactions for AI-enabled products and services.

Assessed by AI UX prototype evaluation, practical design assignment, and rubric-based assessment evaluating application and adaptation of UX and HCI methods using AI design tools and frameworks.

5.3 Design interface elements such as visual indicators or textual cues that communicate AI behaviour, limitations, or confidence levels to users, and evaluate usability, explainability, and trust using methods like user testing or heuristics.

Assessed by AI interface mock-ups, heuristic evaluation report, and peer/instructor review assessing communication of AI behaviour, limitations, confidence levels, usability, explainability, and trust.

5.4 Assess ethical implications of AI-driven user experiences by considering issues such as bias, fairness, and transparency.

Assessed by AI ethics assessment report, written case analysis, and ethical decision reflection assessing ethical implications of AI-driven user experiences, including bias, fairness, and transparency.

4.1 Analyse organisational, user, and regulatory requirements in AI product contexts, such as enterprise software or consumer apps.

Assessed by AI requirements analysis document, submission and review by instructor/stakeholders, assessed for analysis of organisational, user, and regulatory requirements in AI product contexts such as enterprise software or consumer apps.

4.2 Design, evaluate, and justify human-centred UX solutions by working with multidisciplinary teams in AI development projects.

Assessed by Multidisciplinary AI UX package, team project deliverables, and portfolio evaluation, assessed for design, evaluation, and justification of human-centred UX solutions and evidence of multidisciplinary team work.

4.3 Apply industry standards and ethical guidelines such as ISO/IEC 25010 or AI ethics frameworks to ensure transparency, usability, and responsible AI interaction.

Assessed by AI standards compliance report, rubric-based assessment, and audit of artefacts, assessed for application of industry standards and ethical guidelines such as ISO/IEC 25010 or AI ethics frameworks to transparency, usability, and responsible AI interaction.

4.4 Assess and manage UX-related risks in AI deployment contexts, such as autonomous systems or recommendation engines, including loss of user trust or unintended user behaviour.

Assessed by AI UX risk assessment report, scenario analysis, and case study evaluation, assessed for identification, assessment, and management of UX-related risks in AI deployment contexts, including loss of user trust or unintended user behaviour.

4.5 Recommend and defend UX design decisions to stakeholders in AI projects, using data from user studies or metrics.

Assessed by AI stakeholder recommendation report, presentation to stakeholders, and written justification, assessed for data-supported recommendation and defence of UX design decisions using user studies or metrics.

4.6 Support implementation and continuous improvement of AI-enabled UX solutions by integrating feedback from users and regulatory updates.

Assessed by AI implementation feedback log, reflective report, and evidence of iterative improvement, assessed for integration of user feedback and regulatory updates in the implementation and continuous improvement of AI-enabled UX solutions.

3.1 Analyse organisational, user, and regulatory requirements in AI product contexts, such as enterprise software or consumer apps.

Assessed by AI requirements analysis document, submitted for instructor/stakeholder review, assessed for analysis of organisational, user, and regulatory requirements in AI product contexts.

3.2 Design, evaluate, and justify human-centred UX solutions by working with multidisciplinary teams in AI development projects.

Assessed by Multidisciplinary AI UX package, team project deliverables, and portfolio evaluation assessed for design, evaluation, and justification of human-centred UX solutions.

3.3 Apply industry standards and ethical guidelines such as ISO/IEC 25010 or AI ethics frameworks to ensure transparency, usability, and responsible AI interaction.

Assessed by AI standards compliance report, rubric-based assessment, and audit of artefacts assessing application of industry standards and ethical guidelines to transparency, usability, and responsible AI interaction.

3.4 Assess and manage UX-related risks in AI deployment contexts, such as autonomous systems or recommendation engines, including loss of user trust or unintended user behaviour.

Assessed by AI UX risk assessment report, scenario analysis, and case study evaluation assessing UX- related risks, including loss of user trust or unintended user behaviour in AI deployment contexts.

3.5 Recommend and defend UX design decisions to stakeholders in AI projects, using data from user studies or metrics.

Assessed by AI stakeholder recommendation report, presentation to stakeholders, and written justification assessed for evidence-based recommendation and defence of UX design decisions using user studies or metrics.

3.6 Support implementation and continuous improvement of AI-enabled UX solutions by integrating feedback from users and regulatory updates.

Assessed by AI implementation feedback log, reflective report, and evidence of iterative improvement assessed for integration of user feedback and regulatory updates into AI-enabled UX solutions.