AI Literacy Scale for Undergraduate Design Students

Development and Validation

Bibliographic Data

ID22006409
AuthorsShuang Lin (0000-0002-3439-2837, Taizhou University, corresponding author), Tan Zhengtang (0000-0002-0862-311X, Hunan Normal University)
Year2026
Volume16
Issue2
Publication date2026-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/21582440261463131
OpenAlexW7166709480
LanguageEN
References cited54

Artificial intelligence (AI) is reshaping design education, yet field-specific instruments to assess AI literacy remain scarce. Drawing on UNESCO’s AI Competency Framework together with Design Thinking, Systems Thinking and human–AI co-creativity, this study reconceptualizes AI literacy for undergraduate design students and develops the AI-CIEI Scale as a four-dimensional measurement tool. An initial item pool was generated through literature synthesis and expert review, and then refined via cognitive interviews and pilot testing. A survey of 485 design majors from eight public universities in China (5-point Likert scale) was conducted; exploratory factor analysis and confirmatory factor analysis supported a 25-item structure comprising Cognition & Collaboration, Implementation & Integration, Ethics & Critical Judgment, and Innovation & Systemic Thinking, with excellent model fit, high internal consistency, and satisfactory convergent and discriminant validity. Criterion-related validity was examined using a MIMIC-type structural equation model including gender, academic year, major, AI training frequency, and AI usage duration as predictors: academic year showed small but consistent positive associations with all four dimensions, whereas the other variables displayed weak and non-significant effects. These findings indicate that AI literacy in design education reflects intertwined cognitive, procedural, ethical and systemic competencies shaped more by program-level learning trajectories than by short-term exposure, and position the AI-CIEI Scale as a theoretically grounded tool for diagnostics, curriculum alignment and future intervention studies

Cognition · Confirmatory factor analysis · Curriculum · Exploratory factor analysis · Instructional design · Likert scale · Literacy · Metacognition · Structural equation modeling · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Teaching and Learning Programming

Tools

Open DOIOpen Access