ARTIFICIAL INTELLIGENCE AND CRITICAL THINKING: CHALLENGES AND OPPORTUNITIES IN ECUADORIAN HIGHER EDUCATION
Additional Files
Abstract
Artificial intelligence (AI) is transforming the global educational landscape, breaking paradigms towards new and better ways of teaching and learning. In this context, for Ecuadorian higher education, these innovations present both opportunities and challenges, particularly in the development of critical thinking. The increasing reliance on automated tools can lead to a decrease in students' ability to critically analyze and question information. The objective of this study is to analyze the influence of AI on the development of critical thinking in higher education in Ecuador. A non-experimental design with a mixed approach was used, through the application of a structured questionnaire with 10 questions to 150 students and 20 teachers of the computer science program of the Philosophy faculty of the University of Guayaquil. The results: 98% of the respondents value AI positively, highlighting its ability to personalize learning and increase motivation. However, 75% of participants express concern about over-reliance on these technologies, which could limit the development of critical skills. Only 40% feel that their critical thinking has been strengthened, while 55% note that the simplification of access to information has reduced their ability to formulate complex questions. It is concluded that, although AI offers important benefits, it is crucial to implement educational strategies that encourage reflection and deep analysis, ensuring that this technology is used as a complementary tool that enhances, rather than replaces, critical thinking.
##plugins.themes.default.displayStats.downloads##
References
Albert, M.J. (2007). La investigación educativa. Madrid: McGraw Hill.
Anderson, T. (2019). The impact of artificial intelligence on higher education: A theoretical framework. Education and Information Technologies, 24(3), 2091-2102.
Baker, T., & Smith, L. (2019). Educating for the AI era. Journal of Educational Technology, 16(2), 78-92.
Barrows, H. S. (1996). Problem-based learning in medical education: A 40-year retrospective. Higher Education Research & Development, 15(3), 299-307.
Bennett, S. (2022). Artificial Intelligence in Education: Opportunities and Challenges. Computers & Education, 194, 104000.
Black, P., & Wiliam, D. (2009). Developing formative assessment in science teaching. Assessment in Education: Principles, Policy & Practice, 15(3), 267-284.
Brazuelo, F. (2013). El teléfono móvil: actitudes, usos y posibilidades educativas. Tesis doctoral. Universidad Nacional de Educación a Distancia. Madrid, España.
Brookfield, S. D. (2017). Becoming a critically reflective teacher. John Wiley & Sons.
Brusilovsky, P., & Millán, E. (2019). User Modeling in Adaptive Hypermedia Systems. In User Modeling and User-Adapted Interaction (Vol. 28, pp. 3-4). Springer.
Clares, J. (2006). Informática Aplicada a la Investigación Educativa. Sevilla: Digital Atres.
Colás, M. y Buendía, L. (1998). Investigación Educativa. Sevilla: Alfar
Chetouani, A., Piquard, M., & Ciardelli, I. (2019). The Role of Teachers in AI-Enhanced Learning Environments. International Journal of Artificial Intelligence in Education, 29(4), 680-697.
Cukurova, M., Luckin, R., & Millwood, R. (2020). The role of artificial intelligence in transforming education. British Journal of Educational Technology, 51(6), 2047-2061.
Dewey, J. (1933). How We Think. D.C. Heath & Co.
Epstein, D. (2020). Amplitud (Range): por qué los generalistas triunfan en un mundo especializado. EDICIONES URANO.
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284.
Facione, P. A. (2011). Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction. Research Findings, 1-23.
Halpern, D. F. (2014). Thought and Knowledge: An Introduction to Critical Thinking. Psychology Press.
Hmelo-Silver, C. E. (2004). Problem-based learning: An instructional
León, O. y Montero, I. (2004). Métodos de investigación en Psicología y Educación. Madrid: McGraw-Hill.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. penguin UK.
Perrotta, C., & Williamson, B. (2018). The social implications of algorithmic transparency in education: Privacy, surveillance, and social control. Learning, Media and Technology, 43(2), 227-235. https://doi.org/10.1080/17439884.2018.1472600
Ruíz, J. (1995). El Estudio de Casos. Fundamentos y Metodología. Madrid: UNED.
Thomas, M., & McCormick, C. (2021). AI-powered learning: Transforming education through personalization. Educational Technology & Society, 24(4), 32-45.
Wang, X., & Hernández, P. (2020). Human and machine collaboration in education: Insights into effective teaching practices. Journal of Educational Technology Development and Exchange, 13(3), 15-29.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Science and Research: Revista Ciencia e Investigación
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Los autores mantienen los derechos sobre los artículos y por tanto son libres de compartir, copiar, distribuir, ejecutar y comunicar públicamente la obra bajo las condiciones siguientes:
Reconocer los créditos de la obra de la manera especificada por el autor o el licenciante (pero no de una manera que sugiera que tiene su apoyo o que apoyan el uso que hace de su obra).
No utilizar esta obra para fines comerciales.
Declaración de privacidad
Los nombres y direcciones de correo-e introducidos en esta revista se usarán exclusivamente para los fines declarados por esta revista y no estarán disponibles para ningún otro propósito u otra persona.
.JPG)
.png)
