IJIET 2026 Vol.16(3): 585-592
doi: 10.18178/ijiet.2026.16.3.2530
doi: 10.18178/ijiet.2026.16.3.2530
Identifying Distinct AI Literacy Profiles in Higher Education: Implications for Tailored Pedagogical Strategies
Emmanuel Magallanes Ulloa1,*, José Iván López-Flores2, and Carolina Carrillo García2
1. Ingeniería Industrial, Universidad Politécnica de Zacatecas, Fresnillo, México
2. Unidad Académica de Matemáticas, Universidad Autónoma de Zacatecas, Zacatecas, México
Email: emagallanes@upz.edu.mx (E.M.U.); jlopez@uaz.edu.mx (J.I.L.-F.); ccarrillo@uaz.edu.mx (C.C.G.)
*Corresponding author
2. Unidad Académica de Matemáticas, Universidad Autónoma de Zacatecas, Zacatecas, México
Email: emagallanes@upz.edu.mx (E.M.U.); jlopez@uaz.edu.mx (J.I.L.-F.); ccarrillo@uaz.edu.mx (C.C.G.)
*Corresponding author
Manuscript received August 26, 2025; revised September 28, 2023; accepted December 1, 2025; published March 10, 2026
Abstract—Understanding how university students engage with Artificial Intelligence (AI) is essential for designing effective educational strategies. Although global interest in AI for education continues to grow, empirical evidence on students’ levels and profiles of AI literacy remains limited. This study identifies and characterizes distinct AI Literacy profiles among 392 university students in Mexican higher education. A 25-item instrument, validated through Confirmatory FactorAnalysis (CFA), assessed five dimensions of AI literacy:knowledge and skills, emotional engagement, ethical awareness,contextual application, and academic experience. K-meansclustering was then applied to identify latent profiles within thestudent population. Three profiles emerged: disconnectedstudents (44.1%), who showed minimal engagement across alldimensions; curious observers (36.7%), who demonstrated highpractical interest but only moderate conceptual understanding;and informed skeptics (19.1%), who displayed strongconceptual and ethical awareness but limited practicalapplication. Cluster membership showed significantassociations with gender and computing-related academicbackground. The findings highlight substantial heterogeneity instudents’ relationships with AI and underscore the need fordifferentiated pedagogical approaches. The study providesempirical evidence that a uniform model of AI education isinsufficient and emphasizes the importance of addressingdiverse learner needs to support the development of capable,critical, and equitable participation in an AI-driven future.
Keywords—Artificial Intelligence (AI) literacy, cluster analysis, higher education, educational technology, artificial intelligence
Copyright © 2026 by the authors. 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 (CC BY 4.0).
Keywords—Artificial Intelligence (AI) literacy, cluster analysis, higher education, educational technology, artificial intelligence
Cite: Emmanuel Magallanes Ulloa, José Iván López-Flores, and Carolina Carrillo García, "Identifying Distinct AI Literacy Profiles in Higher Education: Implications for Tailored Pedagogical Strategies," International Journal of Information and Education Technology, vol. 16, no. 3, pp. 585-592, 2026.
Copyright © 2026 by the authors. 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 (CC BY 4.0).