Using AI to rethink assessment in health and social care

A key challenge in delivering Health and Social Care qualifications is the growing pressure on assessor capacity to provide high-quality, person-centred assessment. Professional discussions and workplace observations are central to these qualifications but are time intensive, creating bottlenecks that limit scalability and increase costs. This is particularly acute for Level 2 learners, who often need more formative feedback and support to build confidence and practical understanding. As a result, providers face increasing difficulty maintaining assessment quality while also meeting the demand for care workers.

This project will develop a voice-enabled AI system to support professional discussions within assessment. It will compare AI-led and human-led discussions using NCFE-aligned criteria. The system will use speech-to-text technology and a structured “assessor persona” to generate targeted questions aligned to assessment rubrics. Learners will respond verbally, with the system analysing responses and producing structured outputs against assessment criteria.

This approach combines innovation and a human centred-approach with strong sector alignment, including engagement with NCFE to ensure relevance and rigour. If successful, it will reduce assessor workload while providing repeatable, consistent, high-quality professional discussions and feedback, particularly important for learners who lack confidence. At scale, it could expand access to Level 2 Health and Social Care qualifications without compromising quality. By generating comparative evidence and exploring ethical considerations, the project is well positioned to support more scalable, accessible delivery of assessment and explore the wider use of AI in vocational assessment.