Application of Generative Artificial Intelligence to Strengthen Autonomous Learning in University Students
Abstract
The incorporation of generative artificial intelligence tools in higher education has
transformed the dynamics of learning and academic production. The objective of this
research was to analyze the impact of using generative artificial intelligence on strengthening
the autonomous learning of university students. A quantitative methodology with a descriptive-correlational scope was developed, applying a structured questionnaire to 120
students from engineering and administrative science programs at a Colombian higher
education institution. The results showed that 82% of the participants use AI tools at least
three times a week for academic activities, while 76% believe that these technologies
improve subject comprehension and productivity. A positive correlation was also identified
between the frequent use of AI and the development of self-learning skills, academic
organization, and problem-solving abilities. However, risks associated with technological
dependence and a decrease in in-depth critical reading were also detected. It is concluded
that generative artificial intelligence can become a strategic pedagogical resource when its
implementation is accompanied by ethical and methodological guidelines that promote
critical thinking and meaningful learning.
References
Psychological Association (7th ed.). APA Publishing.
• Area, M., & Adell, J. (2019). Tecnologías digitales y cambio educativo. Revista
Interuniversitaria de Formación del Profesorado, 33(2), 13-28.
• Bernal, C. A. (2016). Metodología de la investigación (4.ª ed.). Pearson Educación.
• Cabero, J., & Valencia, R. (2021). Inteligencia artificial y educación: retos y
oportunidades. Revista Científica de Educomunicación, 29(67), 25-34.
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests.
Psychometrika, 16(3), 297-334.
• Hernández Sampieri, R., & Mendoza, C. (2018). Metodología de la investigación: las
rutas cuantitativa, cualitativa y mixta. McGraw-Hill.
• Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial Intelligence in Education: Promises
and Implications for Teaching and Learning. Center for Curriculum Redesign.
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An
Argument for AI in Education. Pearson Education.
• Salinas, J. (2020). Innovación educativa y apropiación tecnológica en la educación
superior. Revista Iberoamericana de Educación, 82(1), 45-62.
• Siemens, G. (2018). Learning analytics and the future of higher education.
• Educational Technology & Society, 21(3), 1-10.
• Tamayo y Tamayo, M. (2017). El proceso de la investigación científica. Limusa.
UNESCO. (2021). Recomendación sobre la ética de la inteligencia artificial.
Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura.
