In this episode of SimCast, Lawrence Hill and Tony Jermy explore how simulation educators can use large language models to work smarter, not harder.

Rather than focusing on high-cost or bespoke AI simulation platforms, this conversation stays firmly grounded in the pragmatic, everyday use of tools such as ChatGPT and Copilot to support the realities of simulation faculty workloads. The discussion centres on “back-of-house” applications that reduce cognitive load, improve consistency, and free up time for what really matters: learners, facilitation, and quality improvement.

#SimCast #Simulation #Podcast #Healthcare #ClinicalSimulation #HigherEducation

Timestamps

00:00 – Welcome to SimCast and episode overview
01:07 – Why this episode exists: time pressure and simulation reality
02:07 – Using AI in simulation: beyond the obvious scenario writing
02:36 – From AI novelty to embedded daily practice
03:02 – ChatGPT vs Copilot: honest reflections and frustrations
04:03 – Apple Intelligence, branding brilliance, delivery… less so
04:25 – How our use of AI for scenario writing has evolved
05:08 – Why paying for AI matters: documents, memory, and projects
05:27 – Using projects and settings to tailor AI for simulation work
06:06 – Starting with a blank screen: AI as a scenario kick-starter
06:23 – The non-negotiable role of fact-checking and human judgement
06:47 – Designing simulations from learning outcomes backwards
07:32 – Standardising simulation documentation with AI templates
07:51 – AI for faculty-facing preparation and organisation
08:23 – AI as a personal assistant for busy simulation educators
09:06 – Preparing learners for high-stakes simulation assessments
09:47 – Scaling individualised rehearsal opportunities for students
10:33 – Accuracy, hallucinations, and student-facing risks
11:32 – Working smarter vs working ethically with AI
12:02 – Why human intelligence still matters
12:29 – Using AI to address gaps in confidence and capability
13:22 – Naïve vs sophisticated use of AI in education
13:43 – AI as an executive assistant, not a subject expert
14:11 – Learning how AI thinks by watching it fail
14:35 – Being polite to AI… and telling it when it gets it wrong
15:09 – When AI fails at “simple” tasks: counting numbers
16:00 – AI as the ultimate people-pleaser
16:42 – What’s next: creative and advanced uses of AI in simulation
17:00 – Final reflections and call to action