Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Although computerized expert models (i.e., algorithms) could improve educational decisions and judgments, initial research has demonstrated that teachers, like other professional groups, tend to be “algorithm averse.” In the current study, we use behavioral and questionnaire data to examine the extent to which in-service and pre-service (i.e., students in training to become) teachers accept advice from expert models and investigate how teachers' acceptance of expert models could be improved. Although it is often presumed that younger generations are less algorithm averse, we demonstrate that both in-service and pre-service teachers prefer advice from a human source (school counselor) than...

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