Inteligência artificial no processo ensino-aprendizagem em ciências da saúde: um estudo compreensivo e avaliativo
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Universidade Federal de Viçosa
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Objetivo: analisar como as ferramentas de Inteligência Artificial são percebidas e utilizadas por docentes e discentes dos cursos de Ciências da Saúde no processo de ensino-aprendizagem. Métodos: trata-se de estudo com uma problemática do tipo mista (quantitativa e qualitativa). O estudo foi desenvolvido em três etapas. Primeiramente, houve a aplicação de questionários online do tipo Likert a 304 discentes dos cursos de Ciências da Saúde de uma instituição pública de ensino superior da Zona da Mata de Minas Gerais, Brasil, compreendendo os cursos de Enfermagem, Educação Física, Medicina e Nutrição, com a coleta de dados realizada entre os meses de abril a maio de 2025. Realizou-se a análise descritiva e inferencial com comparação das respostas entre os diferentes cursos pelo teste de Kruskal Wallis. Em seguida, realizou-se entrevistas individuais com roteiro preestabelecido com 10 docentes dos cursos supramencionados, entre julho e setembro de 2025. Determinou-se a amostragem por conveniência. A análise dos dados seguiu os pressupostos da Análise de Conteúdo, estruturada em três etapas interdependentes e integrada ao uso do software Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires (IRaMuTeQ) versão 0.8 alpha 7. Por fim, foram desenvolvidos dois produtos técnicos: um ebook e um podcast. Estudo aprovado pelo Comitê de Ética sob o parecer nº 7.283.361. Resultados: na etapa quantitativa, 48,4% dos discentes eram da Educação Física; 23,0% da Enfermagem; 15,5% da Medicina e 13,2% da Nutrição. A mediana de idade foi de 22,5 anos. O conhecimento dos discentes sobre as ferramentas de inteligência artificial ainda é incipiente, mas ressalta os benefícios da tecnologia. Os impactos positivos no processo ensino-aprendizagem foram destacados pelos discentes de Enfermagem e Medicina (p=0,028). Quanto à utilização dessas ferramentas, destacaram-se o auxílio na compreensão do conteúdo e esclarecimento de dúvidas, a fim de reduzir erros e tempo, e de facilitar os estudos. A satisfação com a interface das ferramentas foi destacada mais frequentemente pelos discentes de Enfermagem e Medicina (p=0,002). Quanto à legalidade, o receio em utilizar dados provenientes de inteligência artificial foi mencionado em maior frequência pelos discentes de Medicina (p=0,001) e a preocupação com privacidade e segurança dos dados pelos discentes de Enfermagem e Nutrição (p=0,003). Na etapa qualitativa, 30,0% dos docentes eram do curso de Enfermagem, 30,0% da Medicina, 20,0% da Nutrição e 20,0% da Educação Física. A média de idade foi de 41 anos. O corpus registrou 25656 ocorrências e 829 formas ocorreram apenas uma vez, correspondendo a 3,2% do total. Após a lematização, foram identificadas 1747 formas ativas e 126 formas complementares. A Classificação Hierárquica Descendente resultou em cinco classes lexicais, que foram organizadas em três categorias analíticas. Todas as palavras do dendrograma apresentaram nível de significância (p < 0,001). Ebook sobre boas práticas para o uso de inteligência artificial. Podcast sobre contrapontos da IA. Conclusão: as ferramentas de inteligência artificial vêm sendo significativamente utilizadas pelos participantes, mas ainda de forma incipiente. Reconhece-se que as mesmas são importantes mecanismos auxiliares dos discentes e da maioria dos docentes nas atividades educacionais. Palavras-chave: inteligência artificial; ensino; aprendizagem; ciências da saúde; universidade
Objective: to analyze how Artificial Intelligence tools are perceived and used by teachers and students of Health Sciences courses in the teaching-learning process. Methods: this is a mixed-methods study (quantitative and qualitative). The study was developed in three stages. First, Likert-type online questionnaires were administered to 304 students in Health Sciences courses at a public higher education institution in the Zona da Mata region of Minas Gerais, Brazil, comprising Nursing, Physical Education, Medicine, and Nutrition courses, with data collection carried out between April and May 2025. Descriptive and inferential analysis was performed, comparing the responses between the different courses using the Kruskal Wallis test. Next, individual interviews were conducted with a pre-established script with 10 teachers from the above-mentioned courses between July and September 2025. Convenience sampling was determined. Data analysis followed the assumptions of Content Analysis, structured in three interdependent stages and integrated with the use of the software Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires (IRaMuTeQ) version 0.8 alpha 7. Finally, two technical products were developed: an ebook and a podcast. Study approved by the Ethics Committee under opinion No. 7,283,361. Results: in the quantitative stage, 48.4% of the students were from Physical Education; 23.0% from Nursing; 15.5% from Medicine and 13.2% from Nutrition. The median age was 22.5 years. The students' knowledge of artificial intelligence tools is still incipient, but highlights the benefits of the technology. The positive impacts on the teaching-learning process were highlighted by Nursing and Medicine students (p=0.028). Regarding the use of these tools, the students highlighted their usefulness in understanding content and clarifying doubts, in order to reduce errors and time, and to facilitate their studies. Satisfaction with the tools' interface was highlighted more frequently by Nursing and Medicine students (p=0.002). Regarding legality, fear of using data from artificial intelligence was mentioned more frequently by Medicine students (p=0.001), and concern about data privacy and security was mentioned more frequently by Nursing and Nutrition students (p=0.003). In the qualitative stage, 30.0% of the teachers were from the Nursing course, 30.0% from Medicine, 20.0% from Nutrition, and 20.0% from Physical Education. The average age was 41 years. The corpus recorded 25,656 occurrences, and 829 forms occurred only once, corresponding to 3.2% of the total. After lemmatization, 1,747 active forms and 126 complementary forms were identified. The Descending Hierarchical Classification resulted in five lexical classes, which were organized into three analytical categories. All words in the dendrogram had a level of significance (p < 0.001). Ebook on best practices for the use of artificial intelligence. Podcast on AI counterpoints. Conclusion: artificial intelligence tools have been used significantly by participants, but still in an incipient way. It is recognized that they are important auxiliary mechanisms for students and most teachers in educational activities. Keywords: artificial intelligence; teaching; learning; health sciences; university
Objective: to analyze how Artificial Intelligence tools are perceived and used by teachers and students of Health Sciences courses in the teaching-learning process. Methods: this is a mixed-methods study (quantitative and qualitative). The study was developed in three stages. First, Likert-type online questionnaires were administered to 304 students in Health Sciences courses at a public higher education institution in the Zona da Mata region of Minas Gerais, Brazil, comprising Nursing, Physical Education, Medicine, and Nutrition courses, with data collection carried out between April and May 2025. Descriptive and inferential analysis was performed, comparing the responses between the different courses using the Kruskal Wallis test. Next, individual interviews were conducted with a pre-established script with 10 teachers from the above-mentioned courses between July and September 2025. Convenience sampling was determined. Data analysis followed the assumptions of Content Analysis, structured in three interdependent stages and integrated with the use of the software Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires (IRaMuTeQ) version 0.8 alpha 7. Finally, two technical products were developed: an ebook and a podcast. Study approved by the Ethics Committee under opinion No. 7,283,361. Results: in the quantitative stage, 48.4% of the students were from Physical Education; 23.0% from Nursing; 15.5% from Medicine and 13.2% from Nutrition. The median age was 22.5 years. The students' knowledge of artificial intelligence tools is still incipient, but highlights the benefits of the technology. The positive impacts on the teaching-learning process were highlighted by Nursing and Medicine students (p=0.028). Regarding the use of these tools, the students highlighted their usefulness in understanding content and clarifying doubts, in order to reduce errors and time, and to facilitate their studies. Satisfaction with the tools' interface was highlighted more frequently by Nursing and Medicine students (p=0.002). Regarding legality, fear of using data from artificial intelligence was mentioned more frequently by Medicine students (p=0.001), and concern about data privacy and security was mentioned more frequently by Nursing and Nutrition students (p=0.003). In the qualitative stage, 30.0% of the teachers were from the Nursing course, 30.0% from Medicine, 20.0% from Nutrition, and 20.0% from Physical Education. The average age was 41 years. The corpus recorded 25,656 occurrences, and 829 forms occurred only once, corresponding to 3.2% of the total. After lemmatization, 1,747 active forms and 126 complementary forms were identified. The Descending Hierarchical Classification resulted in five lexical classes, which were organized into three analytical categories. All words in the dendrogram had a level of significance (p < 0.001). Ebook on best practices for the use of artificial intelligence. Podcast on AI counterpoints. Conclusion: artificial intelligence tools have been used significantly by participants, but still in an incipient way. It is recognized that they are important auxiliary mechanisms for students and most teachers in educational activities. Keywords: artificial intelligence; teaching; learning; health sciences; university
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SOARES, Francina Lana. Inteligência artificial no processo ensino-aprendizagem em ciências da saúde: um estudo compreensivo e avaliativo . 2025. 121 f. Dissertação (Mestrado em Ciências da Saúde) - Universidade Federal de Viçosa, Viçosa. 2025.