Abstract:

As artificial intelligence (AI) techniques/products develop, the readiness of AI technologies for an AI-infused future has become an important issue for academics and practitioners in the context of university commerce and management education. Understanding the predictors of students’ readiness for AI technologies and their subsequent impact is critical to enabling instructors to develop effective ways to enhance AI education. Considering the limitation of existing self-report instruments, the aim of this study is first to develop and validate a multi-dimensional and generic instrument, the AI literacy attitude scale, which captures individual knowledge and skills on AI technique/product usage by using exploratory and confirmatory scale development approaches. In detail, this study defines and develops the construct of AI literacy attitudes and discusses the theoretical and practical applications of the scale. Furthermore, to improve the generalizability of the AI literacy attitude scale, an online survey will be conducted to collect research data for developing the AI literacy attitude scale. This study seeks to divide the development of the AI literacy attitude scale into four stages: item generation, scale purification, validity testing, and norm establishment. The procedures for conceptualizing the survey, creating the measurement items, collecting data, and validating the scale are described. The reliability, convergent validity, discriminant validity, content validity, criterion-related validity, and nomological validity of the constructs and relationships are thoroughly tested. In essence, this study states that these sub-dimensions may include AI knowledge/skills acquisition, AI knowledge/skills evaluation, AI environment awareness, AI-related technology use & application, learning from AI experience, AI ethics and so on. Additionally, this study explores perceptions of the psychological antecedents and consequences of AI technology readiness concerning individuals’ motivation to use AI technology and learning effectiveness. The findings are expected to provide some important theoretical and practical implications for AI education’s promotion, teaching, learning, and performance evaluation in the context of university commerce and management education.

Keywords:

Artificial intelligence literacy attitudes, Scale development, University commerce and management education.