Artificial intelligence and critical thinking in basic education students
DOI:
https://doi.org/10.56200/mentor.v5i5.13967Keywords:
artificial intelligence, critical thinking, basic education, educational technology, information evaluationAbstract
Generative artificial intelligence has introduced new challenges for checking information and explaining academic decisions in basic education. The objective was to understand the perceptions and educational practices reported by 38 teachers at private schools in Quito regarding artificial intelligence and critical thinking. A qualitative, descriptive-interpretive study examined responses to 14 questions from a semi-structured interview guide. The resulting corpus comprised 532 responses, which were coded and organized through reflexive thematic analysis. Five themes were identified: pedagogical boundaries for using artificial intelligence, verification of generated information, evidence of reasoning, tensions between autonomy and dependence, and institutional conditions. Teachers described checking sources, correcting fabricated quotations or inaccurate data, and requiring oral explanations and demonstrations of student work. They also reported differences in classroom rules and a need for subject-specific training. It was concluded that, in the teachers’ accounts, critical engagement with artificial intelligence depended on checking answers, justifying decisions, and pedagogical guidance. The interview data did not allow direct assessment of students’ critical-thinking performance.
Downloads
References
Alfarwan, A. (2025). Generative AI use in K-12 education: A systematic review. Frontiers in Education, 10, Article 1647573. https://doi.org/10.3389/feduc.2025.1647573
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE Publications.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cheah, Y. H., Lu, J., & Kim, J. (2025). Integrating generative artificial intelligence in K-12 education: Examining teachers’ preparedness, practices, and barriers. Computers and Education: Artificial Intelligence, 8, Article 100363. https://doi.org/10.1016/j.caeai.2025.100363
Gonsalves, C. (2026). Generative AI’s impact on critical thinking: Revisiting Bloom’s taxonomy. Journal of Marketing Education, 48(1). https://doi.org/10.1177/02734753241305980
Lehtinen, A., Channa, F., Näykki, P., Ahlström, E., & Hiljanen, M. (2026). Generative AI for collaborative learning: Fostering critical thinking in teacher education. Journal of Computer Assisted Learning, 42(3), e70256. https://doi.org/10.1002/jcal.70256
Lin, X., & Tan, H. (2025). A systematic review of generative AI in K–12: Mapping goals, activities, roles, and outcomes via the 3P model. Systems, 13(10), Article 840. https://doi.org/10.3390/systems13100840
Liu, J., Sihes, A. J. B., & Lu, Y. (2025). How do generative artificial intelligence (AI) tools and large language models (LLMs) influence language learners’ critical thinking in EFL education? A systematic review. Smart Learning Environments, 12, Article 48. https://doi.org/10.1186/s40561-025-00406-0
Marzano, D. (2025). Generative artificial intelligence (GAI) in teaching and learning processes at the K-12 level: A systematic review. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09853-7
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Song, Z., Qin, J., Jin, F., Cheung, W. M., & Lin, C.-H. (2025). A case study of teachers’ generative artificial intelligence integration processes and factors influencing them. Teaching and Teacher Education, 165, Article 105157. https://doi.org/10.1016/j.tate.2025.105157
Zhang, J., Peng, Y., Deng, X., Zeng, Q., & Wang, K. (2026). The impact of unscaffolded GenAI use on pre-service teachers’ AI readiness, self-regulated learning, critical thinking, and instructional design performance: A quasi-experimental study. Behavioral Sciences, 16(7), Article 1114. https://doi.org/10.3390/bs16071114
Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357. https://doi.org/10.1093/intqhc/mzm042
Zhao, Y., Yue, Y., Sun, Z., Jiang, Q., & Li, G. (2025). Does generative artificial intelligence improve students’ higher-order thinking? A meta-analysis based on 29 experiments and quasi-experiments. Journal of Intelligence, 13(12), Article 160. https://doi.org/10.3390/jintelligence13120160
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Coraima Micaela Valdiviezo Arellano , Jeferson Marcelo Grijalva Rosero , María Fernanda Grijalva Rosero , Alvaro Alexander Viteri Teran , Danny Javier Vinueza Pillajo , Nelson Fernando Ramón Calvache

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.





























+593987341121 