A Pilot Study Investigating STEM Learners' Ability to Decipher AI-generated Video

Published in: American Society for Engineering Education (2021)

Posted on RAND.org on August 12, 2021

by Dule Shu, Christopher Joseph Doss, Jared Mondschein, Denise Kopecky, Valerie A. Fitton-Kane, Lance Bush, Conrad Tucker

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Artificial Intelligence (AI) techniques such as Generative Neural Networks (GNNs) have resulted in remarkable breakthroughs such as the generation of hyper-realistic images, 3D geometries, and textual data. This work investigates the ability of STEM learners and educators to decipher AI generated video in order to safeguard the public-availability of high-quality online STEM learning content. The COVID-19 pandemic has increased STEM learners' reliance on online learning content. Consequently, safeguarding the veracity of STEM learning content is critical to ensuring the safety and trust that both STEM educators and learners have in publicly-available STEM learning content. In this study, state of the art AI algorithms are trained on a specific STEM context (e.g., climate change) using publicly-available data. STEM learners are then presented with AI-generated STEM learning content and asked to determine whether the AI-generated output is visually convincing (i.e., "looks real") and whether the context being presented is plausible. Knowledge gained from this study will help enhance society's understanding of AI algorithms, their ability to generate convincing video output, and the threat that those generated output have in potentially deceiving STEM learners who may be exposed to them during online learning activities.

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