Palavras-chave
Inteligência Artificial na Educação
Aprendizes Multilíngues
Viés Algorítmico
ISO 9241-210
Como Citar
Resumo
Contexto: Embora a inteligência artificial seja promovida como uma força transformadora na educação por meio de plataformas adaptativas e feedback em tempo real, sua implementação para aprendizes multilíngues frequentemente corre o risco de reforçar desigualdades educacionais. A literatura atual alerta que sistemas automatizados ainda apresentam dificuldades com nuances culturais e expressões idiomáticas, evidenciando a necessidade de abordagens de design que priorizem a equidade e a agência humana.
Objetivo: Esta revisão sistemática examina 10 estudos centrais por meio da lente do framework de Design Centrado no Humano ISO 9241-210. O objetivo é analisar como tecnologias educacionais baseadas em IA são projetadas e avaliadas para apoiar aprendizes multilíngues, com foco específico nas fases de "Contexto de Uso", "Requisitos do Usuário", "Soluções de Design" e "Avaliação".
Métodos: Por meio de busca sistemática em quatro bases de dados e subsequente técnica de snowballing, 10 artigos centrais foram selecionados para analisar como ferramentas de IA abordam a diversidade linguística e cultural.
Resultados: Nos estudos revisados, as melhorias relatadas variaram desde ganhos quantitativos, incluindo aumento de 25–31% na retenção de literacia e vocabulário e elevação das taxas de sucesso acadêmico para até 77,8%. Além disso, sistemas orientados por IA, quando alinhados aos princípios de DUH, foram associados a uma economia de até 41% do tempo dos educadores. No entanto, as avaliações revelaram "paradoxos sociotécnicos" críticos: os estudantes enfrentaram um dilema no qual revertiam para prompts em inglês devido ao viés algorítmico em línguas de baixos recursos, e riscos de "preguiça metacognitiva" emergiram da dependência excessiva de ferramentas automatizadas.
Conclusão: A integração bem-sucedida da IA em contextos multilíngues depende de uma mudança da geração de conteúdo para o scaffolding pedagógico. Designers devem priorizar modelos "Human-in-the-Loop" que equilibrem a eficiência computacional com a supervisão humana, de modo a proporcionar o engajamento emocional e a autenticidade cultural que a IA atualmente não é capaz de oferecer.
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