Factors Affecting the Attitude Towards AI Learning: Moderating Role of Information Management

Authors

  • Song Bai College of History and Culture Jilin Normal University Siping, 136000, China

DOI:

https://doi.org/10.3145/epi.2024.ene.0518%20

Keywords:

AI Learning, AI Self-efficacy, Digital Divide, Information Management, Cognitive Absorption.

Abstract

Artificial intelligence integrated with technology has evolved into a domain offering both challenges and opportunities. Artificial intelligence (AI) has enhanced learning and aided human intelligence, but it is solely dependent upon several associated factors. By using the structural equation modeling techniques, this study examines the direct relationships between factors like digital divide, cognitive absorption, AI anxiety, AI self-efficacy, and AI learning, and the moderating role of information management. Additionally, this study also examined how all these factors influence the employees working in the service sector of China. A questionnaire was used to observe the respondents' feedback on a five-point Likert scale. The descriptive results revealed that responses have a mixed mean trend, while the standard deviation in the average scores was also less than 1. Further analysis showed that the average variance extracted for all of variables was above 0.50; the composite reliability was above 0.80 but items having less loadings were deleted, thus confirming the reliability of items. The final results show that the digital divide and information management promoted AI learning, whereas AI Anxiety impedes all types of learning among employees in the service sector. The moderating effect of information management also exists between AI self-efficacy and AI learning and between the digital divide and AI learning in this cohort of respondents.

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Published

2024-12-17

How to Cite

Song Bai. (2024). Factors Affecting the Attitude Towards AI Learning: Moderating Role of Information Management. Profesional De La información, 33(5). https://doi.org/10.3145/epi.2024.ene.0518

Issue

Section

Research articles