Using Generative Artificial Intelligence and Keyword Analysis to Support the Design of Research Projects in Higher Education
DOI:
https://doi.org/10.17398/1695-288X.24.2.87Keywords:
Co-word analysis, Generative Artificial Intelligence, Research Scope, Research Project, Higher EducationAbstract
At different levels of university education, students face challenges in locating bibliographic references that align with their research projects, requiring a balance between specificity and breadth in identifying relevant information. Studies show that metacognitive skills, such as keyword refinement and navigation through organized categories, enhance the efficiency and quality of search processes. In light of these challenges, this article investigates the combined use of co-word analysis and generative artificial intelligence (GenAI) as a strategy to support the definition of research project scopes in higher education. The study adopted a mixed-methods approach, involving 23 undergraduate and graduate students, who responded to two forms interspersed with the delivery of a personalized report. This report was produced through searches in bibliographic databases integrated with GenAI analysis and addressed aspects such as topic adequacy, keyword suggestions, search strategies, and a glossary of concepts. The results indicated that the procedure stimulated reflective processes among students, with the majority considering the intervention useful, particularly in expanding thematic understanding and refining the definition of keywords. It also became evident that the effectiveness of the method depends on the clarity of the initial information provided by the participants and the maturity level of their research projects.
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