Supplementary Materials for: "A Tridimensional Model of AI Literacy: An Empirical Analysis of Student Performance and Demographic Patterns in Higher Education"
Year: 2025
Type: supplementary-materials
Abstract: This Zenodo record provides the dataset and supplementary materials for the study "A tridimensional model of AI literacy: An empirical analysis of student performance and demographic patterns in higher education." Description This research moves beyond theoretical frameworks to empirically analyze artificial intelligence (AI) literacy among 353 undergraduate students at a private university in Mexico. The study utilized a validated, performance-based instrument to assess the functional, technical, and socio-critical competencies of the students. The analysis revealed three distinct student profiles: lower performance, mid-range proficiency, and higher competence. A key finding was a significant deficit in the socio-critical dimension of AI literacy across all profiles, with only 2.6% of students demonstrating outstanding ability. The study also found that these profiles varied significantly by gender and academic stage, challenging traditional assumptions about technology literacy. These findings provide an evidence-based typology for diagnosing student needs and developing targeted, equitable educational strategies to foster comprehensive AI literacy in higher education. The study has several implications for practice and policy: Curriculum leaders should view AI literacy as a transversal competence, integrating ethical and critical reflection across curricula. Educators and instructional designers must apply differentiated instruction based on learner profiles. Policymakers should promote validated assessment tools to guide institutional strategies and resource allocation for AI education. Contents Appendix A: Sample Items for the AI Competencies Evaluation: This document provides illustrative examples of the performance-based test items used to assess the functional, technical, and socio-critical dimensions of AI literacy. The items have been translated from the original Spanish. Appendix B: Psychometric analysis and validation: This appendix details the psychometric analysis of the performance-based assessment instrument using a one-parameter logistic (1PL) Rasch model. It includes the Test Information Curve (TIC), histograms of Infit and Outfit statistics, a Person-Item Map, and histograms of plausible values by subscale (Functional, Sociocritical, Technical). The analysis confirms the instrument's overall integrity, unidimensional structure, and reliability (EAP reliability = 0.703), demonstrating its effectiveness in capturing a wide range of student abilities and providing a reliable basis for the study's findings. Appendix C: AI Literacy Competency Analytical Rubric: This document provides a comprehensive analytical rubric designed to assess AI literacy competency. It outlines three performance levels (Not Yet Satisfactory, Satisfactory, and Outstanding) across three key dimensions: Functional, Technical, and Sociocritical. The rubric also includes detailed feedback recommendations for each level and dimension, offering constructive guidance for student development. This tool can be used to interpret the assessment results and inform targeted educational interventions.
Authors: Medina-Gual, Luis, Medina-Velázquez, Luis, José-Luis, Parejo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7104378036
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