Quantum mechanics

Nomological network

Scale (ratio)

Cognitive psychology

Construct (python library)

Chromatography

Discriminant validity

Psychiatry

Developmental psychology

Power (physics)

Computer science

Anxiety

Psychometrics

Structural equation modeling

Physics

Construct validity

Psychology

Incremental validity

Convergent validity

Data science

Reliability (semiconductor)

Artificial intelligence

Chemistry

Programming language

Sample (material)

Machine learning

Internal consistency

Interactive Learning Environments

While increasing productivity and economic growth, the application of artificial intelligence (AI) may ultimately require millions of people around the world to change careers or improve their skills. These disruptive effects contribute to the general public anxiety toward AI development. Despite the rising levels of AI anxiety (AIA) in recent decades, no AI anxiety scale (AIAS) has been developed. Given the limited utility of existing self-report instruments in measuring AIA, the aim of this paper is to develop a standardized tool to measure this phenomenon. Specifically, this paper introduces and defines the construct of AIA, develops a generic AIAS, and discusses the theoretical and practical applications of the instrument. The procedures used to conceptualize the survey, create the measurement items, collect data, and validate the multi-item scale are described. By analyzing data obtained from a sample of 301 respondents, the reliability, criterion-related validity, content validity, discriminant validity, convergent validity, and nomological validity of the constructs and relationships are fully examined. Overall, this empirically validated instrument advances scholarly knowledge regarding AIA and its associated behaviors.

Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior