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(How) Do We Teach Emotions?

 

Emotional intelligence constitutes a key component of human capital, shaped partially by educational materials. Using machine learning and generative AI tools, we examine emotional content in public-school textbooks and children’s literature. A stark mismatch emerges: text exposes children to a broad emotional range, but images overwhelmingly depict happiness and calm, regardless of emotions described in text on the same page. Nearly half of pages show zero overlap between the emotions described in text and those shown in images. This pattern persists across time, contexts, and identities. Household purchases and library inventories suggest content may be endogenously shaped by consumer demand favoring "positive" cover imagery, implying market forces narrow the emotional landscape children encounter.

Keywords
emotions, culture, content analysis, education policy, curriculum, artificial intelligence tools, computational social science, natural language processing, large language models, computer vision
Education level
Document Object Identifier (DOI)
10.26300/hgn9-n385
EdWorkingPaper suggested citation:
Adukia, Anjali, Matthew Bonci, Paula Dastres Gallardo, Emileigh Harrison, Jake Nicoll, and Teodora Szasz. (). (How) Do We Teach Emotions?. (EdWorkingPaper: -1584). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/hgn9-n385

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