Linear regression model to predict the use of artificial intelligence in experimental science students
Full Text (PDF)
Elizeth Mayrene Flores Hinostroza 1 2 , Derling Jose Mendoza 2 3 * , Mercedes Navarro Cejas 1 2 , Edinson Patricio Palacios Trujillo 2 4
More Detail
1 Universidad Tecnica de Manabi, Portoviejo, ECUADOR2 Universidad Nacional de Chimborazo, Riobamba Canton, ECUADOR3 Universidad Nacional de Educación, Chuquipata Sector, ECUADOR4 Universidad Estatal de la Península de Santa Elena, La Libertad, ECUADOR* Corresponding Author

Abstract

This study builds on the increasing relevance of technology integration in higher education, specifically in artificial intelligence (AI) usage in educational contexts. Background research highlights the limited exploration of AI training in educational programs, particularly within Latin America. AI has become increasingly pivotal in educational practices, influencing the development of competencies in various disciplines, including experimental sciences. This study aimed to describe the correlation between professional competencies in AI, AI usage, and digital resources among students in the experimental sciences education program at the National University of Chimborazo. Methodologically, a quantitative approach was employed, involving a structured survey distributed among 459 students. Data analysis was conducted using multiple regression models to establish predictive insights into AI usage. A multiple linear regression model was developed to predict AI usage among these students. The analysis revealed significant correlations between AI competencies, AI usage, and digital resources. The regression model highlighted that both AI competencies and digital resources are significant predictors of AI usage. These findings underscore the importance of developing AI competencies and providing access to digital resources to enhance the effective use of AI in educational practices. Limitations and future research directions are discussed.

Keywords

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

INT ELECT J MATH ED, Volume 20, Issue 1, February 2025, Article No: em0807

https://doi.org/10.29333/iejme/15736

Publication date: 01 Jan 2025

Online publication date: 20 Dec 2024

Article Views: 41

Article Downloads: 15

Open Access References How to cite this article