Poster Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2026

Using AI-simulated conversations to build cancer workforce communication capability (146536)

Kyleigh Smith 1 2 , Michelle Barrett 2 , Sharon De Graves 2 3 , Erin Turner 2 , Kathe Holmes 2 , Caitlin Broomhall 2 , Goran Duric 2
  1. Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
  2. VCCC Alliance, Melbourne, VIC, Australia
  3. Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia

Background: Cancer care requires clinicians to navigate complex, sensitive and highly individualised conversations. Simulation is an established approach to developing healthcare communication skills; however, traditional simulated patient programs can be resource-intensive and difficult to scale. Emerging evidence suggests generative artificial intelligence (AI)-enabled virtual patients can provide dynamic, repeatable and personalised opportunities for experiential learning and communication practice.1,2,3

Aim: To evaluate the feasibility of integrating AI-powered simulated patient conversations into digital cancer education and explore its potential as a model for workforce communication capability development.

Methods: The VCCC Alliance integrated the SimConverse AI platform into an existing digital learning environment, using the Sexual Health and Cancer course, launched in July 2026, as an implementation case study. Learners engage with virtual patients in realistic clinical scenarios, receive immediate AI-generated feedback and can repeat conversations to refine their communication skills in a psychologically safe environment. Platform analytics and learner evaluation assess engagement, repeat practice, confidence, user experience and perceived impact on practice.

Results: Early implementation demonstrates the feasibility of embedding AI simulation within cancer education infrastructure. Initial analytics demonstrate learner uptake and repeat attempts, enabling deliberate, self-directed practice independent of educator availability. Platform analytics and learner engagement, will be presented.

Conclusion: AI-enabled simulation provides an opportunity to move cancer education beyond knowledge acquisition towards experiential, practice-based learning at scale. Sexual health provides an initial implementation case study; however, the model has potential application across cancer care where communication capability is critical, including difficult conversations, informed consent, palliative and end-of-life care, shared decision-making and culturally responsive care. This implementation contributes real-world evidence to an emerging field and provides a practical model for integrating AI-enabled communication practice within existing cancer workforce education.

  1. Jiang J, Ye MZ, Kwok TTO, Wong JYH. (2026). GenAI-Supported Virtual Patients in Health Care Education: Systematic Review. Journal of Medical Internet Research.
  2. Kaplonyi J, Bowles KA, Nestel D, et al. (2017). Understanding the impact of simulated patients on health care learners' communication skills: a systematic review. Medical Education, 51(12), 1209–1219.
  3. Hicke Y, et al. (2025). MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education.