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Perfis docentes e inteligência artificial no ensino superior no Estado do Paraná, Brasil
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Abstract(s)
Este artigo apresenta uma análise exploratória de dados realizada com uma amostra de 611 docentes pertencentes a sete universidades estaduais públicas do Paraná. A partir da aplicação de técnicas de inteligência artificial (IA) e ciência de dados, os participantes foram classificados em três grupos (clusters), considerando quatro dimensões analíticas: percepção de competência em IA, reconhecimento da urgência do tema nas áreas de ensino, pesquisa, extensão e gestão, postura ética frente ao uso da IA e interesse em formação continuada. Foram utilizadas as técnicas de Análise de Componentes Principais (PCA) e de clusterização por meio do algoritmo K-Means. A análise resultou na identificação de três perfis distintos: “veteranos cautelosos” (ou tradicionalistas), “realistas abertos” (possíveis articuladores institucionais) e “novos entusiastas” (potenciais multiplicadores). Conclui-se pela necessidade de formulação de políticas formativas segmentadas e flexíveis, com percursos formativos adaptados aos perfis identificados.
This article presents an exploratory data analysis conducted with a sample of 611 faculty members from seven public state universities in Paraná, Brazil. By applying artificial intelligence (AI) and data science techniques, participants were classified into three groups (clusters) based on four analytical dimensions: perceived AI competence, recognition of the urgency of the topic across teaching, research, outreach, and management areas, ethical stance toward AI use, and interest in continuing education. Principal Component Analysis (PCA) and clustering using the K-Means algorithm were employed. The analysis identified three distinct profiles: cautious veterans (or traditionalists), open realists (potential institutional articulators), and new enthusiasts (potential multipliers). The findings highlight the need for segmented and flexible training policies, with learning pathways tailored to the identified profiles.
This article presents an exploratory data analysis conducted with a sample of 611 faculty members from seven public state universities in Paraná, Brazil. By applying artificial intelligence (AI) and data science techniques, participants were classified into three groups (clusters) based on four analytical dimensions: perceived AI competence, recognition of the urgency of the topic across teaching, research, outreach, and management areas, ethical stance toward AI use, and interest in continuing education. Principal Component Analysis (PCA) and clustering using the K-Means algorithm were employed. The analysis identified three distinct profiles: cautious veterans (or traditionalists), open realists (potential institutional articulators), and new enthusiasts (potential multipliers). The findings highlight the need for segmented and flexible training policies, with learning pathways tailored to the identified profiles.
Description
Keywords
Competências em IA Educação e tecnologia Análise multivariada AI competencies Education and technology Multivariate analysis