The increasing use of Generative Artificial Intelligence in Higher Education has created new challenges for academic integrity, intellectual authorship, and the development of critical thinking. This study analyzes these challenges and evaluates a pedagogical intervention designed to promote the responsible use of Generative Artificial Intelligence in graduate education. The study was conducted between February 2025 and June 2026 and involved a total of 50 graduate students from three cohorts enrolled in two Master’s programs in Engineering and Business Administration. A qualitative educational action research approach was adopted, based on the analysis of academic assignments, similarity reports, and Artificial Intelligence-assisted writing detection using Turnitin, together with classroom observations and reflective discussions. The pedagogical intervention incorporated strategies based on the UNESCO (2023) guidance, structured prompt design, and the Socratic model. The findings revealed frequent use of Generative Artificial Intelligence-generated content without adequately paraphrasing the generated material, verifying information, or consulting the scientific literature. Following the intervention, students demonstrated greater attention to question formulation, information validation, and the use of reliable academic sources. The study contributes empirical evidence from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
Rodrigo Florencio da Silva· Information· 0 citations
Pre-trained contextual language models have demonstrated excellent performance in training with data from new languages and tasks. For this purpose, additional pre-training is necessary, since the lack of vocabulary of the language always tends to degrade the results. In this article, we present a procedure to deal with and treat unknown languages or languages without available resources such as Kicongo, a Bantu matrix language, commonly spoken in the northern regions of Angola, with greater incidence in rural regions and in the countries of central Africa (Democratic Republic of Congo, Republic of Congo and Gabon) and throughout the world. With the extension of Natural Language Processing (NLP) models based on the Bidirectional Encoder Representations from Transformers (BERT) architecture (BERT, RoBERTa and DistilBERT), the performance evaluation of the models was carried out using metrics such as accuracy, precision, recall and f1-score, achieving a performance rate higher than 97%.
Henriques Fernando, Rodrigo Florencio da Silva· Revista Brasileira de Comput...· 0 citations