AI Quality Improvement Apprentice

An AI-first development role for someone who is genuinely passionate about emerging technology and wants to apply it to real business challenges.

You will work within the Quality Team while also helping Projecting Success identify practical opportunities to use AI and automation to work smarter and grow.

Quality assurance, data & management information:

• Maintain quality dashboards, reports and management information to support decision-making.
• Use AI/automation to analyse quality findings, identify trends and highlight risks, good practice and improvements.
• Analyse learner data other quality indicators to inform improvements.
• Produce clear reports and visualisations to help managers monitor quality, learner outcomes and inspection readiness.
• Create and maintain quality trackers, action plans, self-assessment documents and improvement records.
• Maintain accurate, timely quality information with effective version control and audit trails.

Inspection, audit & compliance readiness:

• Prepare for inspections, audits and funding reviews by collating and maintaining evidence.
• Maintain accessible evidence libraries and inspection-readiness records.
• Use digital tools and automation to track actions, evidence, deadlines and follow-ups.
• Identify record gaps or inconsistencies for review by the relevant lead.
• Produce management information and evidence packs for internal and external quality activity.

AI-assisted quality assurance & assessment:

• Develop AI-assisted tools to review learner files, assessment decisions and compliance requirements.
• Test and evaluate AI solutions that improve quality assurance consistency, accuracy and efficiency.
• Develop AI marking and feedback tools that support assessors while retaining professional judgement, human oversight and quality standards.
• Develop tools that review written feedback, flag evidence gaps and improve assessment feedback consistency and quality.
• Develop and test AI-assisted moderation and standardisation tools to improve assessment consistency.
• Contribute to systems that flag possible inappropriate or unethical AI use for human review; indicators must not be treated as conclusive evidence.
• Document testing outcomes, limitations, risks and recommended controls for AI-assisted quality tools.

Digital systems & automation:

• Develop digital systems that improve quality monitoring, reporting, record-keeping and access to management information.
• Work with quality and curriculum colleagues to identify administrative processes suitable for automation.
• Map processes and support the design, testing and implementation of controlled automated workflows.
• Ensure digital quality systems maintain accurate data, audit trails and access controls.
• Identify opportunities to connect information and reduce duplication across quality and curriculum processes.

Curriculum, teaching & learning innovation:

• Research emerging AI technologies that could support teaching, learning, assessment, curriculum and quality improvement.
• Work with curriculum colleagues, tutors and assessors to identify where AI or automation could address practical challenges.
• Support controlled pilots and gather evidence on effectiveness, risks and limitations.
• Develop guidance, examples and resources for responsible, effective AI use in teaching, learning and assessment.
• Share relevant AI developments, learning and good practice.

Responsible AI & information handling:

• Use AI and data in line with Projecting Success policies, data protection, information security and AI governance.
• Maintain confidentiality.
• Build human review into AI processes affecting learners, assessment, quality or compliance.
• Raise concerns about AI bias, inaccuracy, data protection risks or over-reliance on automated outputs.

• Communication skills
• IT skills
• Attention to detail
• Organisation skills
• Creative
• Initiative
• Passion for AI & automation