Proposta metodológica para a escrita acadêmica assistida por inteligência artificial: o marco CO–STAR

Autores

DOI:

https://doi.org/10.46219/rechiem.v17i3.206

Palavras-chave:

Inteligência artificial, Inovações nos métodos de ensino, Ética da ciência, Litmaps, Consensus

Resumo

Este artigo apresenta uma proposta metodológica para apoiar a escrita de ensaios acadêmicos por meio do uso pedagógico de ferramentas de inteligência artificial (IA), especificamente o ChatGPT integrado ao marco CO–STAR (Contexto, Objetivo, Estilo, Tom, Audiência e Resposta) para o design de prompts. A pesquisa corresponde a um estudo exploratório baseado na implementação formativa dessa proposta, documentado por meio de um estudo de caso instrumental com dois estudantes de um doutorado em Didática da Matemática. O processo foi desenvolvido em quatro fases: (1) diagnóstico das habilidades de escrita, (2) design de prompts com o CO–STAR, (3) aplicação em sessões síncronas e (4) avaliação por meio de apresentações orais. Os resultados evidenciam melhorias na organização das ideias, no registro acadêmico, na clareza textual e na apropriação crítica do uso da IA. Além disso, foram identificadas aprendizagens relacionadas à integridade acadêmica, à revisão de fontes e ao posicionamento do autor no texto. Conclui-se que essa proposta favorece uma escrita reflexiva e ética, promovendo uma relação crítica com tecnologias emergentes. Ademais, demonstra potencial de transferência para outros contextos formativos e abre novas linhas de investigação sobre escrita acadêmica, IA e formação investigativa em educação matemática.

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Biografia do Autor

José Luis Morales Reyes, Pontificia Universidad Católica de Valparaíso

Candidato a Doutor em Didática da Matemática na Pontificia Universidad Católica de Valparaíso e Mestre em Matemática Educativa pelo CINVESTAV do México (estudos presenciais, em regime de tempo integral). Em 2021, recebeu menção honrosa do Prêmio Simón Bolívar de melhor dissertação de mestrado, concedida pelo Comitê Latino-Americano de Matemática Educativa (CLAME).

Além disso, possui experiência como professor de matemática no ensino superior e na educação secundária (ciclo diversificado, Bacharelado Internacional, MATEM, coordenador de departamento). No âmbito universitário, atuou na UCR, UNED e UNA, ministrando disciplinas de serviço e componentes matemáticos e didáticos na formação de professores.

Por outro lado, participou como palestrante em diversos e relevantes congressos internacionais, tais como: International Congress on Mathematical Education (ICME), Congress of the European Society for Research in Mathematics Education (CERME), Conference of the International Group for the Psychology of Mathematics Education (PME) e a Reunião Latino-Americana de Matemática Educativa (RELME).

Brahiam Ramírez, Pontificia Universidad Católica de Valparaíso

Professor de Matemática e estudante de doutorado em Didática da Matemática na Pontificia Universidad Católica de Valparaíso, Chile.

Marcerla Parraguez González, Pontificia Universidad Católica de Valparaíso

Doutora em Matemática Educativa e professora titular na Pontificia Universidad Católica de Valparaíso, Chile.

Referências

Ahmed, I., Liu, W., Roscoe, R. D., Reilley, E. y McNamara, D. S. (2025). Multifaceted assessment of responsible use and bias in language models for education. Computers, 14(3), 1–12. https://doi.org/10.3390/computers14030100

Beel, J., Gipp, B., Langer, S. y Breitinger, C. (2016). Research–paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305–338. https://doi.org/10.1007/s00799–015–0156–0

Bekker, M. (2024). Large language models and academic writing: Five tiers of engagement. South African Journal of Science, 120(1/2), 1–5. https://doi.org/10.17159/sajs.2024/17147

Bender, E. M., Gebru, T., McMillan–Major, A. y Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922

Bista, K. y Bista, R. (2025). Leveraging AI tools in academic writing: Insights from doctoral students on benefits and challenges. American Journal of STEM Education: Issues and Perspectives, 6, 32–47. https://doi.org/10.32674/9m8dq081

Bouzar, A., El Idrissi, K. y Ghourdou, T. (2024). ChatGPT and academic writing self–efficacy: Unveiling correlations and technological dependency among postgraduate students. Arab World English Journal, 15, 225–236. https://doi.org/10.24093/awej/chatgpt.15

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027

Butson, R. y Spronken–Smith, R. (2024). AI and its implications for research in higher education: A critical dialogue. Higher Education Research & Development, 43(3), 563–577. https://doi.org/10.1080/07294360.2023.2280200

Calle–Arango, L. y Ávila Reyes, N. (2023). Obstacles, facilitators, and needs in doctoral writing: A systematic review. Studies in Continuing Education, 45(2), 133–151. https://doi.org/10.1080/0158037X.2022.2026315

Castañeda, A. y Sánchez, M. (2025). ¿Se vale usar ChatGPT si igual entendí el tema? Una conversación urgente sobre IA en el aula. Revista Enseñanza de las Matemáticas y Experiencias Docentes, 1(2), 9–14. https://doi.org/10.24844/REMED/0102.00

Chen, Y.C. (2019). Writing as an epistemological tool: Perspectives from personal, disciplinary, and sociocultural landscapes. En V. Prain y B. Hand (Eds.), Theorizing the future of science education research (pp. 115–132). Springer. https://doi.org/10.1007/978–3–030–24013–4_8

Cordón, Ó. (2023). Inteligencia artificial en educación superior: Oportunidades y riesgos. Revista Interuniversitaria de Investigación en Tecnología Educativa, 15, 16–27. https://doi.org/10.6018/riite.591581

Cox, A. (2024). Algorithmic literacy, AI literacy and responsible generative AI literacy. Journal of Web Librarianship, 18(3), 93–110. https://doi.org/10.1080/19322909.2024.2395341

Delgadillo, I. y Beel, J. (2019). Towards reproducible research in recommender systems for research papers. Proceedings of the 13th ACM Conference on Recommender Systems, 498–502. https://doi.org/10.1145/3298689.3347043

Dempere, J., Modugu, K. y Ramasamy, L. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1–13. https://doi.org/10.3389/feduc.2023.1206936

Flick, U. (2018). An introduction to qualitative research (6th ed.). SAGE Publications.

Fuchs, K. (2023). Exploring the opportunities and challenges of NLP models in higher education: ¿Is ChatGPT a blessing or a curse? Frontiers in Education, 8, 1–4. https://doi.org/10.3389/feduc.2023.1166682

Hutson, J. (2024). Rethinking plagiarism in the era of generative AI. Journal of Intelligent Communication, 3(2), 20–31. https://doi.org/10.54963/jic.v3i2.220

Jain, S., Kumar, A., Roy, T., Shinde, K., Vignesh, G. y Tendulkar, R. (2024). SciSpace literature review: Harnessing AI for effortless scientific discovery. En N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald y I. Ounis (Eds.), Advances in Information Retrieval (pp. 256–260). Springer. https://doi.org/10.1007/978–3–031–56069–9_28

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A. y Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730

Karakose, T. (2023). The utility of ChatGPT in educational research—Potential opportunities and pitfalls. Educational Process: International Journal, 12(2), 7–13. https://doi.org/10.22521/edupij.2023.122.1

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, J., Sailer, M., Schmidt, A., Sedlmeier, P., Spinner, B., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

Lee, S. S. y Moore, R. L. (2024). Harnessing generative AI for automated feedback in higher education: A systematic review. Online Learning, 28(3), 82–105. https://files.eric.ed.gov/fulltext/EJ1446868.pdf

Maynez, J., Narayan, S., Bohnet, B. y McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 1906–1919). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl–main.173

Mugaanyi, J., Sekajja, S. y Ndagire, S. (2024). Evaluation of large language model performance and reliability for citations and references in scholarly writing: Cross–disciplinary study. Journal of Medical Internet Research, 26, e52935. https://doi.org/10.2196/52935

Nava-Guzmán, C. (2025). La inteligencia artificial generativa en la enseñanza de las matemáticas. Revista Enseñanza de las Matemáticas y Experiencias Docentes, 1(2), 77–89. https://doi.org/10.24844/REMED/0102.05

Nguyen, A., Hong, Y., Dang, B. y Huang, X. (2024). Human–AI collaboration patterns in AI–assisted academic writing. Studies in Higher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593

Nguyen, T. Y. P., Nguyen, N. T. y Phan, N. K. H. (2025). The challenges of applying ChatGPT in the academic writing of postgraduate students in English major at IUH. International Journal of AI in Language Education, 2(1), 45–58. https://doi.org/10.54855/ijaile.25212

Oates, A. y Johnson, D. (2025). ChatGPT in the classroom: Evaluating its role in fostering critical–evaluation skills. International Journal of Artificial Intelligence in Education, 35(1), 45–68. https://doi.org/10.1007/s40593–024–00452–8

OpenAI (2024). ChatGPT (Aug 3 version) [Modelo de lenguaje]. https://chat.openai.com/

Pavlova, N. H. (2024). Flipped dialogic learning method with ChatGPT: A case study. International Electronic Journal of Mathematics Education, 19(1), Article em0764. https://doi.org/10.29333/iejme/14025

Rababah, L. M., Rababah, M. A. y Al–Khawaldeh, N. N. (2024). Graduate students’ ChatGPT experience and perspectives during thesis writing. International Journal of Engineering Pedagogy (iJEP), 14(3), 22–35. https://doi.org/10.3991/ijep.v14i3.48395

Ramírez, B., Morales–Reyes, J. L. y Parraguez, M. (2025). Pauta para la declaración del uso ético y responsable de la inteligencia artificial en investigaciones realizadas por estudiantes universitarios. Manuscrito sometido para publicación.

Román–Acosta, D., Rodríguez Torres, E., Baquedano, M. B., López, L. C. y Pérez, A. (2024). ChatGPT and its use to improve academic writing in postgraduate students. Praxis Pedagógica, 24(36), 53–75. https://doi.org/10.26620/uniminuto.praxis.24.36.2024.53–75

Sánchez, E., Sánchez, M., García, M., Aguayo, L., Valenzuela, C. y Chávez, Y. (2025). Ensayos y revisiones de literatura en Educación Matemática: Caracterización y criterios de evaluación. Educación Matemática, 37(1), 5–8. https://doi.org/10.24844/EM3701.00

Stake, R. E. (2020). Investigación con estudio de casos (6ª ed.). Ediciones Morata.

Suárez–Pizzarello, M., Sánchez–Trujillo, M. D. L. A. y Rodríguez Flores, E. A. (2024). Exploring ChatGPT–4 as an academic assistant in thesis development: A case study on postgraduate higher education. Proceedings of the 2024 IEEE 4th International Conference on Advanced Learning Technologies on Education & Research (ICALTER) (pp. 1–4). IEEE. https://doi.org/10.1109/ICALTER65499.2024.10819226

Sulisworo, D. (2023). Exploring research idea growth with Litmap: Visualizing literature review graphically. Bincang Sains Dan Teknologi, 2(2), 48–54. https://doi.org/10.56741/bst.v2i02.323

Susnjak, T. y McIntosh, T. R. (2024). ChatGPT: The end of online exam integrity? Education Sciences, 14(6), 1–20. https://doi.org/10.3390/educsci14060656

Tran, T. T. P., Dang, T. N. y Nguyen, V. L. P. (2025). Master students’ perceptions of how ChatGPT influenced critical thinking in academic writing at the Industrial University of Ho Chi Minh City. International Journal of AI in Language Education, 2(2), 20–39. https://doi.org/10.54855/ijaile.25222

Vivas.AI. (2024). Mastering prompt engineering: A guide to the CO–STAR and TIDD–EC frameworks. Medium. https://vivasai01.medium.com/mastering-prompt-engineering-a-guide-to-the-co-star-and-tidd-ec-frameworks-3334588cb908

Wahba, F., Ajlouni, A. O. y Abumosa, M. A. (2024). The impact of ChatGPT-based learning statistics on undergraduates’ statistical reasoning and attitudes toward statistics. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), Article em2468. https://doi.org/10.29333/ejmste/14726

Wang, J., Liardét, C. y Lum, J. (2025). Feeling like an academic writer: An exploration of doctoral students’ struggle for recognition. Studies in Continuing Education, 47(1), 285–301. https://doi.org/10.1080/0158037X.2024.2358006

Zapata–Ros, M. (2018). La universidad inteligente: La transición de los LMS a los sistemas inteligentes de aprendizaje en educación superior. Revista de Educación a Distancia, 57(10), 1–43. http://dx.doi.org/10.6018/red/57/10

Publicado

2025-12-31

Como Citar

Morales Reyes, J. L., Ramírez, B., & Parraguez González, M. (2025). Proposta metodológica para a escrita acadêmica assistida por inteligência artificial: o marco CO–STAR. Revista Chilena De Educación Matemática, 17(3), 103–122. https://doi.org/10.46219/rechiem.v17i3.206

Edição

Seção

Artículos de investigación