Palavras-chave
Inteligência Artificial na Educação
Enquadramento Midiático
Tecnologia Educacional
Como Citar
Resumo
Objetivo: Analisar como a mídia tradicional dos Estados Unidos enquadrou a inteligência artificial generativa na educação durante sua fase inicial de difusão, verificando se a cobertura priorizou a definição de problemas e a avaliação moral em detrimento de soluções pedagógicas e inovação institucional.
Metodologia: Estudo de análise de conteúdo com base em dados extraídos da base Dow Jones Factiva. Foram inicialmente identificados 2.369 itens publicados entre janeiro de 2023 e setembro de 2025, dos quais 542 artigos com foco substantivo em educação foram selecionados após critérios de exclusão. A codificação foi realizada com o software Dedoose (Versão 10), permitindo categorização temática e análise comparativa. No total, foram analisadas 3.469 menções codificadas.
Resultados: O enquadramento orientado a riscos foi predominante (21,6%), incluindo temas como desinformação, alucinações, privacidade e integridade acadêmica. Em seguida, destacaram-se percepção pública e debate (15,0%) e indústria/tecnologia educacional (13,4%). A cobertura evidenciou simultaneamente oportunidades (como tutoria e ganhos de eficiência) e preocupações com precaução, com maior ênfase em governança e segurança do que em pedagogia ou formação docente.
Conclusão: O discurso midiático inicial posiciona a inteligência artificial na educação majoritariamente como um desafio de políticas públicas e gestão de riscos, relegando a transformação pedagógica a um papel secundário. A compreensão desses padrões de enquadramento contribui para que educadores e formuladores de políticas equilibrem a adoção de inovações com abordagens cautelosas e regulatórias.
Originalidade/Valor: O estudo oferece evidências empíricas sobre padrões de enquadramento midiático em um período crítico de difusão da IA generativa, contribuindo para o debate sobre governança, percepção pública e integração tecnológica na educação.
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Copyright (c) 2026 Corey H. Basch, Grace C. Hillyer, Victoria H. Wagner, Charles E. Basch