Artificial Intelligence (AI) literacy questionnaire with confirmatory factor analysis

Artificial Intelligence (AI) literacy questionnaire with confirmatory factor analysis

Proceedings of 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023

• Ng, Tsz Kit The University of Hong Kong
• Wu, Wenjie The Chinese University of Hong Kong
• Leung, Jac Ka Lok The Hong Kong University of Science and Technology
• Chu, Samuel Kai Wah The University of Hong Kong

USA

IEEE

2023

• F 教與學
  • FD 傳意媒介、資訊科技在教學上的應用
• L 教育測量及評鑑
  • LC 學業成績測量

AI Machine Learning AI Literacy Psychometric Measurement

• Hong Kong

• 1997.7 onwards

• 中學教育

In recent years, schools started to incorporate artificial intelligence (AI) into computer science/STEAM curricula. However, few validated measurements have been designed to examine how secondary students develop AI literacy and perceive their learning outcomes. AI literacy has been measured from students' knowledge and skill acquisition, and behavior and attitudes. This research aims to develop and validate an instrument to assess AI literacy for secondary students. A questionnaire with 25 items measured in a 5-point Likert scale was created. In a pilot study, the questionnaire was administered to a sample of 363 secondary school students from two different schools in Hong Kong. Confirmatory factor analysis was conducted and grouped into six factors: (1) intrinsic motivation, (2) self-efficacy, (3) behavioral intention, (4) behavioral engagement, (5) know and understand, and (6) use and apply AI. The questionnaire showed a good fit model to support internal consistency reliability in most of the factors, with Cronbach's Alpha levels ranging from.58 to.88. A less-parsimonious model was proposed that help educators measure a wider AI literacy skill set with an acceptable model fit, with Cronbach's Alpha levels ranging from.91 to.94 based on affective, behavorial, cognitive and ethical (ABCE) learning framework. Further studies are needed to confirm the factor structure. Copyright © 2023 IEEE.

• 英文

章節

• 9798350300543

• 10.1109/ICALT58122.2023.00074

https://bibliography.lib.eduhk.hk/tc/bibs/7c9789ac

2025-05-12