From role 1.2 Data Curation Lead · EQF EQF7
Define, evaluate, coordinate, and optimise AI quality strategies by integrating ethical, resilience, and performance considerations across AI systems, using relevant frameworks and monitoring to address complex problems and enhance organisational outcomes.
2.1 Define AI quality strategies by applying principles of ethics, performance, and resilience, using frameworks such as ISO/IEC 42001, EU AI Ethics Guidelines, and Explainable AI best practices.
Assessed by Assessment of an AI training module design document and AI use-case mapping, evaluated for relevance to ICT quality strategy, clarity of learning design, alignment with data quality objectives, and suitability of selected AI use cases.
2.2 Evaluate AI system interactions, interdependencies, and quality objectives against organisational goals, using AI performance metrics, KPIs, and benchmarking tools.
Assessed by Evaluation of AI training materials and a recorded or observed AI training session, assessed for accuracy, pedagogical quality, practical applicability, and ability to communicate ICT quality principles in AI-enabled data curation contexts.
2.3 Coordinate cross-functional activities to implement AI quality strategies and ensure alignment with organisational objectives.
Assessed by Review of an AI learning feedback summary and revised AI training content, assessed for quality of feedback analysis, responsiveness to learner needs, evidence of improvement, and alignment with ICT quality and data governance requirements.
2.4 Optimise AI quality strategies by integrating strategic monitoring, feedback loops, and continuous improvement practices.
Assessed by Assessment of AI ethics and governance training materials and applied AI case examples, evaluated for completeness, regulatory and ethical accuracy, relevance to data quality strategy, and ability to support responsible AI/data curation practice.
2.5 Solve strategic AI quality challenges across multiple systems by applying evidence-based decision-making.
Assessed by Evaluation of documented AI learner support evidence and facilitation notes, assessed for clarity of guidance, appropriateness of support interventions, responsiveness to learner difficulties, and consistency with intended learning outcomes.
2.6 Report and communicate strategic AI quality outcomes, lessons learned, and recommendations for organisational decision-making.
Assessed by Review of an AI instructional design rationale and alignment with learning objectives, assessed for coherence between learning outcomes, activities, assessment approach, ICT quality strategy, and the required professional competence level.