BETWEEN DATA AND MEANINGS: EPISTEMOLOGICAL AND METHODOLOGICAL CONTRIBUTIONS OF ARTIFICIAL INTELLIGENCE TO QUALITATIVE AND QUANTITATIVE RESEARCH
DOI:
https://doi.org/10.61164/c7tkek73Keywords:
Inteligência Artificial; Pesquisa Qualitativa; Pesquisa Quantitativa; Metodologia Científica; Ética na Pesquisa.Abstract
The incorporation of Artificial Intelligence (AI) into the field of scientific research has generated debates regarding its actual methodological, epistemological, and ethical contributions, especially with respect to qualitative and quantitative research. In this context, this article raises the following problem: how can AI contribute to the improvement of methodological processes in scientific research without compromising intellectual autonomy, critical interpretation, and the ethical principles of the researcher? The objective is to critically analyze the potentialities, limitations, and ethical implications of using AI in the production of scientific knowledge. Methodologically, this is an integrative literature review with a qualitative, exploratory, and descriptive approach, grounded in classical and contemporary authors who discuss technology, scientific methodology, and artificial intelligence. The results indicate that AI significantly enhances the capacity for data collection, organization, processing, and analysis, promoting greater analytical accuracy, time optimization, and diversification of interpretative strategies. Tools such as ChatGPT, IRAMUTEQ, and Jamovi are configured as cognitive mediators, capable of supporting researchers in both qualitative and quantitative analyses without replacing human judgment. As an original contribution, the study proposes understanding AI as a complementary methodological device, whose use requires a critical, reflective, and ethical stance. It is concluded that the balanced integration between human and artificial intelligence inaugurates a new paradigm in contemporary scientific research, guided by innovation, interdisciplinarity, and methodological rigor.
Downloads
References
BARDIN, Laurence. Análise de conteúdo. São Paulo: Edições 70, 2016.
BENDER, Emily M. et al. On the dangers of stochastic parrots: Can language models be too big? In: Proceedings of the ACM Conference on Fairness, Accountability, and Transparency. New York: ACM, 2021. p. 610–623. DOI: https://doi.org/10.1145/3442188.3445922
BOTELHO, Louise de Lira Roedel; CUNHA, Cristiano Castro de Almeida; MACEDO, Marcelo. O método da revisão integrativa nos estudos organizacionais. Gestão & Sociedade, Belo Horizonte, v. 5, n. 11, p. 121–136, 2011. DOI: https://doi.org/10.21171/ges.v5i11.1220
CAMARGO, Brígido Vizeu; JUSTO, Ana Maria. IRAMUTEQ: um software gratuito para análise de dados textuais. Temas em Psicologia, Ribeirão Preto, v. 21, n. 2, p. 513–518, 2013. DOI: https://doi.org/10.9788/TP2013.2-16
CHALMERS, Alan. O que é ciência afinal? São Paulo: Brasiliense, 1993.
CRAWFORD, Kate. Atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press, 2021. DOI: https://doi.org/10.12987/9780300252392
CRESWELL, John W. Projeto de pesquisa: métodos qualitativo, quantitativo e misto. Porto Alegre: Artmed, 2010.
DENZIN, Norman K.; LINCOLN, Yvonna S. The Sage handbook of qualitative research. 5. ed. Thousand Oaks: Sage, 2018.
DEDE, Chris. Comparing frameworks for 21st century skills. In: BELLANCA, James; BRANDT, Ron (org.). 21st century skills: rethinking how students learn. Bloomington: Solution Tree Press, 2018. p. 51–76.
DINIZ, Debora et al. Inteligência artificial e ética da pesquisa científica: desafios contemporâneos. Revista Bioética, Brasília, v. 32, n. 1, p. 1–12, 2024.
FIELD, Andy. Discovering statistics using IBM SPSS statistics. 4. ed. London: Sage, 2013.
FIELD, Andy. Discovering statistics using IBM SPSS statistics. 5. ed. London: Sage, 2018.
FLICK, Uwe. Introdução à pesquisa qualitativa. Porto Alegre: Artmed, 2009.
GIL, Antonio Carlos. Métodos e técnicas de pesquisa social. 6. ed. São Paulo: Atlas, 2010.
GIL, Antonio Carlos. Métodos e técnicas de pesquisa social. 7. ed. São Paulo: Atlas, 2019.
JAMES, Gareth et al. An introduction to statistical learning. New York: Springer, 2013. DOI: https://doi.org/10.1007/978-1-4614-7138-7
KOSMYNA, Nataliya et al. Evaluating cognitive and ethical risks of large language models. Nature Human Behaviour, London, v. 9, p. 1–10, 2025.
KUHN, Thomas S. A estrutura das revoluções científicas. São Paulo: Perspectiva, 1998.
LATOUR, Bruno. Ciência em ação: como seguir cientistas e engenheiros sociedade afora. São Paulo: Editora UNESP, 2000.
MAYER, Richard E. Multimedia learning. 2. ed. Cambridge: Cambridge University Press, 2009.
MISHRA, Punya; KOEHLER, Matthew J. Technological pedagogical content knowledge: a framework for teacher knowledge. Teachers College Record, New York, v. 108, n. 6, p. 1017–1054, 2006. DOI: https://doi.org/10.1177/016146810610800610
MONTEIRO, Silvana Drumond; ASSIS, Juliana. Inteligência artificial, epistemologia e ética na pesquisa científica. Revista Educação & Sociedade, Campinas, v. 46, p. 1–18, 2025.
MORIN, Edgar. Introdução ao pensamento complexo. Porto Alegre: Sulina, 2005.
MONTGOMERY, Douglas C.; RUNGER, George C. Applied statistics and probability for engineers. 6. ed. Hoboken: Wiley, 2014.
O’NEIL, Cathy. Weapons of math destruction: how big data increases inequality and threatens democracy. New York: Crown, 2016.
QUALTRICS. Research trends: artificial intelligence in research. Provo: Qualtrics, 2025. Disponível em: https://www.qualtrics.com. Acesso em: 10 nov. 2025.
RODRIGUES, Ana Paula. Inteligência artificial generativa e pesquisa qualitativa: mediações e desafios interpretativos. Revista Brasileira de Pesquisa Qualitativa, São Paulo, v. 13, n. 1, p. 45–62, 2025.
SAMPAIO, Rafael Cardoso; SABBATINI, Rodrigo; LIMONGI, Rafael. Ciência 4.0 e inteligência artificial: impactos na pesquisa científica. Revista Comunicação & Sociedade, Braga, v. 45, p. 1–20, 2024.
SAMPAIO, Rafael Cardoso et al. Inteligência artificial e metodologia científica: desafios contemporâneos. Revista Brasileira de Metodologia Científica, Brasília, v. 8, n. 2, p. 77–95, 2024.
SELWYN, Neil. Should robots replace teachers? AI and the future of education. Cambridge: Polity Press, 2019.
SHMUELI, Galit et al. Data mining for business analytics. Hoboken: Wiley, 2017.
SHMUELI, Galit et al. To explain or to predict? Statistical Science, Hayward, v. 35, n. 3, p. 359–379, 2020.
SIEMENS, George. Learning analytics: the emergence of a discipline. American Behavioral Scientist, Thousand Oaks, v. 57, n. 10, p. 1380–1400, 2013. DOI: https://doi.org/10.1177/0002764213498851
THE JAMOVI PROJECT. Jamovi (Version 2.4) [Computer software]. Sydney, 2023. Disponível em: https://www.jamovi.org. Acesso em: 12 nov. 2025.
THOMAS, Gary. Doing case study research. London: Sage, 2000.
TURING, Alan M. Computing machinery and intelligence. Mind, Oxford, v. 59, n. 236, p. 433–460, 1950. DOI: https://doi.org/10.1093/mind/LIX.236.433
WENGER, Etienne. Communities of practice: learning, meaning, and identity. Cambridge: Cambridge University Press, 1998. DOI: https://doi.org/10.1017/CBO9780511803932
ZAWACKI-RICHTER, Olaf et al. Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, London, v. 16, n. 39, p. 1–27, 2019. DOI: https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MARIA ZULI MORAIS FARIAS DE SOUZA, Marly Otília dos Santos , Kalyne Madeira Furtado, Ana Cláudia Gomes Arraes , Francisco Renato Silva Ferreira, Manoel Pereira da Rocha Neto, Francisco Francinete Leite Júnior , José Eduardo de Carvalho Lima

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
Authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution License, which permits the sharing of the work with proper acknowledgment of authorship and initial publication in this journal;
Authors are authorized to enter into separate, additional agreements for the non-exclusive distribution of the version of the work published in this journal (e.g., posting in an institutional repository or publishing it as a book chapter), provided that authorship and initial publication in this journal are properly acknowledged, and that the work is adapted to the template of the respective repository;
Authors are permitted and encouraged to post and distribute their work online (e.g., in institutional repositories or on their personal websites) at any point before or during the editorial process, as this may lead to productive exchanges and increase the impact and citation of the published work (see The Effect of Open Access);
Authors are responsible for correctly providing their personal information, including name, keywords, abstracts, and other relevant data, thereby defining how they wish to be cited. The journal’s editorial board is not responsible for any errors or inconsistencies in these records.
PRIVACY POLICY
The names and email addresses provided to this journal will be used exclusively for the purposes of this publication and will not be made available for any other purpose or to third parties.
Note: All content of the work is the sole responsibility of the author and the advisor.
