Development and psychometric analysis of 5-item Artificial Intelligence Attitude (AIAT-5) for students

Area of Science:

• Educational Technology
• Psychometrics
• Artificial Intelligence

Background:

• Integrating artificial intelligence (AI) into education requires efficient methods to gauge student AI attitudes.
• Assessing student AI perception is vital for successful educational digital transformation.

Purpose of the Study:

• To develop and validate a concise, five-item Artificial Intelligence Attitude Scale (AIAT-5).
• To establish the reliability and validity of the AIAT-5 for assessing middle and high school students' AI attitudes in China.

Main Methods:

• A survey was administered to 1576 middle and high school students across diverse regions in China.
• Exploratory (EFA) and Generalizability (EGA) Factor Analyses were used in Study 1.
• Confirmatory Factor Analysis (CFA) was employed in Study 2 to validate the scale's structure.

Main Results:

• The AIAT-5 demonstrated strong internal consistency and good discrimination in Study 1, supporting a unidimensional structure.
• Study 2's CFA confirmed an excellent model fit for the unidimensional structure, with high factor loadings.
• Student AI attitude positively correlated with generative AI knowledge, life satisfaction, and epistemic curiosity, but also internet addiction.

Conclusions:

• The AIAT-5 is a reliable and efficient tool for large-scale assessment of AI attitudes among Chinese middle and high school students.
• The scale's validation supports its use in understanding student perceptions of AI within educational contexts.