CARE: Clinical AI Readiness & Reflection Ecosystem
The leap from a university lecture theatre to a busy hospital ward is often the most daunting moment for any healthcare student. While classroom learning provides the theory, the transition to real-world clinical placement can feel fragmented and unpredictable. Students frequently encounter inconsistent supervision or find themselves in high-pressure situations where there is little room to practise delicate professional communication. This "readiness gap" creates a significant challenge: how do we ensure students are truly prepared, confident, and empathetic before they reach a patient's bedside?
Led by Prof. Shara LEE, Associate Professor, Department of Health Technology and Informatics, CARE: Clinical AI Readiness and Reflection Ecosystem serves as a transformative bridge designed to harmonise this journey. By deploying an AI-enabled platform, CARE ensures that students are active, well-prepared participants in their clinical training. The project introduces a flexible, modular system — comprising three independently deployable components — that educators can tailor to any course or timetable. It begins with a digital evolution of the traditional orientation: through interactive, query-based modules, authentic hospital workflows and educator insights are converted into engaging briefings, ensuring every student starts with a consistent foundation of knowledge and significantly reducing the administrative burden on frontline hospital staff.
As students progress, CARE provides a safe yet strikingly realistic arena for growth through immersive communication simulations. Here, learners engage with virtual patients in time-bound dialogues that demand clinical accuracy, genuine empathy, and a professional tone. Crucially, CARE's reflection and analytics component is not confined to this sequence — it can be deployed at any point in the learning journey, including independently during clinical placement itself, providing students with a protected space for structured self-evaluation whether or not they have engaged with the briefing or simulation components. Using AI-driven analytics, the system generates individualised dashboards that visualise a student's progress, giving educators the precise data needed for high-impact, targeted mentoring. This approach builds directly on validated prior work: a predecessor study involving 81 students across PolyU and HKU demonstrated significant gains in professional identity (+0.34), reflective disposition (+0.31), and learning motivation (+0.23), with no equity gaps observed.
Funded at over HKD 1.6 million under the Teaching Development Grant 2025–28, this ecosystem represents a significant leap forward for PolyU Education 4.0, moving beyond simple digital tools to create an AI-empowered, student-centred model of excellence. By linking preparation, action, and reflection into a seamless yet adaptable loop, CARE exemplifies how technology can enhance fundamentally human skills like compassion and resilience. As this project rolls out across eight disciplines, its foundations are anchored in authentic clinical practice — built in close partnership with Princess Margaret Hospital and Prince of Wales Hospital in Hong Kong, and Princess Margaret Cancer Centre in Toronto, Canada. This international collaboration ensures CARE reflects real-world clinical environments across systems and settings, reinforcing PolyU's standing as a world-class innovative institution and demonstrating that data-driven transformation can cultivate a more confident, empathetic, and practice-ready generation of healthcare professionals.
This project is awarded under the Teaching Development Grant 2025–28 in the Theme-based Projects category (Individual Project).
