IEEE Access (Jan 2024)
College Teachers’ Behavioral Intention to Adopt Artificial Intelligence-Assisted Teaching Systems
Abstract
Given the extensive application of artificial intelligence (AI) within the educational domain, college teachers’ behavioral intention to adopt AI-assisted teaching systems (AIATS) has emerged as a critical topic, with gaps in the breadth of samples and the diversity of methods. To further delve into the principal elements affecting college teachers’ inclination towards AIATS, this research constructs an integrative theoretical framework that synthesizes the Technology Acceptance Model (TAM) with the Innovation Diffusion Theory (IDT), while comprehensively considering internal and external factors. The investigation collected sample data from a questionnaire survey, which reached 529 college teachers in China, and undertook data analysis employing SPSS 27.0 and AMOS 26.0. The findings revealed that: 1) college teachers’ behavioral intention to embrace AIATS is directly and significantly shaped by perceived ease of use, perceived usefulness, trust and subjective norms; 2) factors such as observability, relative advantage and compatibility indirectly impact college teachers’ adoption willingness of AIATS through their perceived ease of use and perceived usefulness; 3) complexity of AIATS positively affects perceived ease of use by means of perceived time cost, thereby influencing behavioral intention; 4) sociocultural factors significantly impact the adoption and promotion of AIATS in China. By extending the applicability of TAM and IDT within the realm of educational technology acceptance, this study offers strategic recommendations geared towards educational policymakers and technology developers to promote the implementation of AIATS, and sheds light on educational technology adoption across diverse sociocultural landscapes.
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