Case study on data simulation for academic research using Generative Artificial Intelligence and Google Colab

Authors

DOI:

https://doi.org/10.62697/rmiie.v4iS1.143

Keywords:

Simulated data, generative artificial intelligence, data validation, Python, ChatGPT, prompts, t-Student

Abstract

A case study on data simulation for academic research is presented, where with Generative Artificial Intelligence (IAG) and the ChatGPT model it is possible to obtain a set of simulated data with the appropriate prompts. Google Colab for Python was used for data validation. The methodology used focuses on comparing two sets of data, one that is real and another that is not, but constructed under parameters of the primary data such as the mean, standard deviation and amount of data. Specific Paython libraries such as numpy and ttest_ind were used for the analysis of statistics such as T-Student, and others such as matplotlib and seaborn for density plots. The results show that the simulated data was closely related to the real data. It was shown that the statistical differences were not significant, demonstrating the usefulness of the methodology used.

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Author Biographies

Miguel Ángel Fernández-Marín, Universidad Metropolitana. Ecuador.

 

 

Débora González-Tolmo, Empresa de software en Quito Netby. Ecuador.

 

 

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Published

2025-04-01

How to Cite

Fernández-Marín, M. Ángel, Montero-Murillo, J. R., & González-Tolmo, D. (2025). Case study on data simulation for academic research using Generative Artificial Intelligence and Google Colab. Revista Mexicana De Investigación E Intervención Educativa, 4(S1), 18–26. https://doi.org/10.62697/rmiie.v4iS1.143

Issue

Vol. 4 No. S1 (2025): Competencias docentes en nuevos contextos digitales (abril)

Section

Artículos

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Copyright (c) 2025 Miguel Ángel Fernández-Marín, Jordy Rafael Montero-Murillo, Débora González-Tolmo

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