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Simulation-based learning has become a cornerstone of healthcare education, fostering essential skills like communication, teamwork or decision-making in safe, controlled environments. However, participants’ reflection on simulations often rely on subjective recollections, limiting their effectiveness in promoting learning. This symposium explores how multimodal analytics and AI can enhance simulation-based education by automating teamwork analysis data, providing structured feedback, and supporting reflective practices. The papers examine real-time analytics for closed-loop communication in cardiac arrest simulations, multimodal data use to refine feedback in ICU nursing simulations, generative AI-powered chatbots facilitating nursing students' interpretation of multimodal learning analytics dashboards, and culturally sensitive, AI-based scenarios for Breaking Bad News in an Indian context. Collectively, these contributions highlight the transformative potential of using data and AI-enhanced solutions, emphasizing personalization, cultural sensitivity, and human-centered design, and invite dialogue on the pedagogical, technological and ethical implications of introducing data-based practices and AI-based tools in medical education.
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This content will become publicly available on May 23, 2026
Towards Certified Safe Personalization in Learning Enabled Human-in-the-loop Human-in-the-plant Systems
The paper presents AIIM, an Artificial Intelligence (AI) enabled personalIzation Management software for human-in-the-loop, human-in-the-plant Learning enabled systems (LES). AIIM can be integrated with LES software to aid a human user to achieve safe and effective operation under dynamically changing contexts. AIIM consists of: A) an AI technique to derive model coefficient of a physics guided surrogate model from operational data shared following privacy norms, and b) continuous model conformance to identify key changes in LES operational behavior that may jeopardize safety. We demonstrate two capabilities of AIIM, personalization and unknown error detection, through case studies that span a significant breadth of dynamic context change scenarios including: a) involuntary change in user context such as medication induced glucose metabolism change in automated insulin delivery (AID), b) actuation failure such as cartridge blockage in AID, c) latent sensor error in aviation, and d) unknown coding error in autonomous car software patches. We compare AIIM personalization with human-in-the-loop and self-adaptive model-predictive control design on real-life and simulation settings, to show safe and improved diabetes management.
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- Award ID(s):
- 2436801
- PAR ID:
- 10650901
- Publisher / Repository:
- ACM Transactions on Emerging Technologies in Computing Systems
- Date Published:
- Journal Name:
- ACM Journal on Emerging Technologies in Computing Systems
- ISSN:
- 1550-4832
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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