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Ubiquitous misinformation on social media threatens the health and well-being of young people. We review research on susceptibility to misinformation, why it spreads, and how these mechanisms might operate developmentally. Although we identify many research gaps, results suggest that cognitive ability, thinking styles, and metacognitive scrutiny of misinformation are protective, but early adverse experiences can bias information processing and sow seeds of mistrust. We find that content knowledge is not sufficient to protect against misinformation, but that it, along with life experiences, provides a foundation for gist plausibility (true in principle, rather than true at the level of verbatim details) that likely determines whether misinformation is accepted and shared. Thus, we present a theoretical framework based on fuzzy-trace theory that integrates the following: knowledge that distinguishes verbatim facts from gist (knowledge that is amplified by cognitive faculties and derived from trusted sources); personality as an information-processing filter colored by experiences; emotion as a product of interpreting the gist of information; and ideology that changes prior probabilities and gist interpretations of what is plausible. The young and the old may be at greatest risk because of their prioritization of social goals, a need that social media algorithms are designed to meet but at the cost of widespread exposure to misinformation. 

This paper reviews the developmental literature on decision making, discussing how increased reliance on gist thinking explains the surprising finding that important cognitive biases increase from childhood to adulthood. This developmental trend can be induced experimentally by encouraging verbatim (younger) versus gist (older) ways of thinking. We then build on this developmental literature to assess the developmental stage of artificial intelligence (AI) and how its decision making compares with humans, finding that popular models are not only irrational but they sometimes resemble immature adolescents. To protect public safety and avoid risk, we propose that AI models build on policy frameworks already established to regulate other immature decision makers such as adolescents.