Awards and Recognition

Recognising research excellence and effective communication.

This award celebrates an oral presentation that demonstrates originality, academic rigour, relevance to healthcare education and the ability to communicate findings clearly.

Selection criteria:

• Originality and significance.
• Research design and methodological quality.
• Relevance to AI in healthcare education.
• Clarity and structure of the presentation.
• Engagement with questions and critical discussion.

Selection: Judged on the symposium day by a designated panel using an agreed assessment rubric.

Celebrating research that communicates clearly and makes an impact.

This award recognises a poster that combines a strong contribution with clear visual communication and effective engagement with the audience. Selection criteria:

• Research quality and originality.
• Relevance to symposium themes.
• Clarity and visual organisation.
• Communication of methods, findings and implications.
• Engagement with questions and discussion.

Selection: Judged on the symposium day according to the published criteria.

Celebrating partnerships that advance AI in healthcare education.

Artificial intelligence in healthcare education thrives on collaboration. Bringing together students, academic staff, clinicians, researchers, industry partners and healthcare organisations creates opportunities to combine expertise, challenge established approaches and develop innovative solutions to educational and clinical training challenges.

The Inspiring Collaboration Award celebrates individuals and teams who demonstrate the value of working together to advance AI in healthcare education.

We invite nominations for collaborative projects that demonstrate meaningful partnership, innovative thinking, educational value and the potential to make a positive difference to the preparation of the next generation of healthcare professionals.

Who is eligible?

The award is open to collaborative projects relevant to AI in healthcare education, including:

• Student-staff collaborations: Projects that bring students and academic or professional staff together.
• Interdisciplinary partnerships: Teams combining expertise across different academic or professional disciplines.
• University-healthcare collaborations: Projects involving universities, hospitals, clinical teams or other healthcare organisations.
• University-industry partnerships: Collaborations with technology developers, AI companies or other industry partners.
• Wider organisational collaborations: Projects involving professional bodies, external organisations or other relevant entities.

Projects may be at the development, pilot, implementation or established stage. Nominations should demonstrate genuine collaboration and explain the project's relevance to AI in healthcare education.

Judging criteria

Nominations will be assessed by a designated judging panel against the following criteria:

1. Meaningful collaboration (30%) Evidence of shared goals, active participation, complementary expertise and meaningful contributions from the partners involved.

2. Innovation and originality (20%) The extent to which the project introduces a creative approach to an educational or clinical training challenge.

3. Educational relevance and value (20%) The project's contribution to healthcare education, clinical learning, assessment, faculty development or clinical workforce preparation.

4. Responsible use of AI (15%) Consideration of relevant issues such as ethics, patient safety, fairness, privacy, accountability and the role of human judgement.

5. Impact and future potential (15%) Evidence of achievements, lessons learned, educational benefits or potential for further development, wider application and sustainable impact.

The judging panel will consider projects in the context of their stage of development. Established outcomes are valuable, but early-stage projects may also demonstrate exceptional collaboration, innovation and promise.