Developing and Validating a Scale for Artificial Intelligence Literacy (SAIL): A Mixed Approach

• In Event: AERA Poster Session Friday 7:45 am
  In Poster Session: ATL Poster Session 2: AI Integration and Outcomes in Postsecondary Settings

Abstract

This study presents the development and validation of the Scale for Artificial Intelligence Literacy (SAIL), designed to assess AI literacy in higher education. Using a mixed approach, the study integrated theoretical frameworks (top-down) and learner perspectives (bottom-up) to create a robust, psychometrically validated instrument. Data were collected from 1,231 participants across four cohorts, with exploratory and confirmatory factor analyses confirming a four-factor (competence, ethics, motivation, and self-efficacy), 20-item structure. The SAIL model demonstrates strong validity, reliability, and relevance in the context of generative AI technologies, filling gaps in existing AI literacy assessments. This study provides researchers and educators with a comprehensive tool to evaluate and enhance AI literacy in an AI-driven educational landscape.

Authors

• Michael Yi-Chao Jiang, Shenzhen Technology University

• Sanki Zu-Quan Shao, Chinese University of Hong Kong

• Morris Siu-Yung Jong, Chinese University of Hong Kong

• Ching Sing Chai, Chinese University of Hong Kong

• Chin-Chung Tsai, National Taiwan Normal University