As conversational AI apps such as Siri and Alexa become ubiquitous among children, the CS education community has begun leveraging this popularity as a potential opportunity to attract young learners to AI, CS, and STEM learning. However, teaching conversational AI to K-12 learners remains challenging and unexplored due in part to the abstract and complex nature of some conversational AI concepts, such as intents and training phrases. One promising approach to teaching complex topics in engaging ways is through unplugged activities, which have been shown to be highly effective in fostering CS conceptual understanding without using computers. Research efforts are underway toward developing unplugged activities for teaching AI, but few thus far have focused on conversational AI. This experience report describes the design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners. We discuss learner responses and lessons learned through our implementation of these
unplugged activities. Our hope is that these insights support CS education researchers in making conversational AI learning more engaging and accessible to all learners.
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This content will become publicly available on January 3, 2025
Artificial Intelligence for End Tidal Capnography Guided Resuscitation: A Conceptual Framework
Artificial Intelligence (AI) and machine learning have advanced healthcare by defining relationships in complex conditions. Out-of-hospital cardiac arrest (OHCA) is a medically complex condition with several etiologies. Survival for OHCA has remained static at 10% for decades in the United States. Treatment of OHCA requires the coordination of numerous interventions, including the delivery of multiple medications. Current resuscitation algorithms follow a single strict pathway, regardless of fluctuating cardiac physiology. OHCA resuscitation requires a real-time biomarker that can guide interventions to improve outcomes. End tidal capnography (ETCO2) is commonly implemented by emergency medical services professionals in resuscitation and can serve as an ideal biomarker for resuscitation. However, there are no effective conceptual frameworks utilizing the continuous ETCO2 data. In this manuscript, we detail a conceptual framework using AI and machine learning techniques to leverage ETCO2 in guided resuscitation.
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- Award ID(s):
- 2037398
- NSF-PAR ID:
- 10491302
- Publisher / Repository:
- HICSS Conference Office University of Hawaii at Manoa
- Date Published:
- Journal Name:
- Proceedings of the 57th Hawaii International Conference on System Sciences
- ISSN:
- 2572-6862
- Subject(s) / Keyword(s):
- Artificial intelligence, cardiac arrest, resuscitation, end tidal capnography, reinforcement learning
- Format(s):
- Medium: X
- Location:
- Honolulu HI
- Sponsoring Org:
- National Science Foundation
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