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Updated: Jul 18, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deepfakes and scientific knowledge dissemination.

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Many people cannot identify science deepfakes, with adults and educators being more vulnerable. Understanding this vulnerability is key to combating misinformation in education.

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Area of Science:

  • Digital Media Forensics
  • Educational Technology
  • Science Communication

Background:

  • Science misinformation poses public policy challenges.
  • AI-generated deepfakes increase the risk of manipulated digital content exposure.
  • Educational stakeholders increasingly rely on video for information, heightening deepfake risks.

Purpose of the Study:

  • To investigate education stakeholders' vulnerability to science deepfakes.
  • To identify characteristics moderating this vulnerability.
  • To assess susceptibility across student, educator, and public populations.

Main Methods:

  • Surveyed individuals from five populations (students, educators, public) focusing on climate change deepfakes.
  • Sample nationally representative of three populations.
  • Analyzed vulnerability based on demographics, trust in information, and political orientation.

Main Results:

  • 27-50% of individuals could not distinguish authentic videos from deepfakes.
  • Vulnerability increased with age and trust in information sources.
  • Adults and educators showed higher vulnerability than students; exposure amplified susceptibility.

Conclusions:

  • Education stakeholders, particularly adults and educators, are vulnerable to science deepfakes.
  • Interventions are needed as exposure increases deepfake perniciousness.
  • Addressing the social context of deepfakes is a promising mitigation strategy.