Title: Understanding adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model

Authors: Hao-Chu Lin; Chih-Feng Ho; Han Yang

Addresses: Department of Business Administration, Nation Taipei University of Business, Zhongzheng District, Taipei City, Taiwan ' Department of International Business, Nation Taipei University of Business, Zhongzheng District, Taipei City, Taiwan ' School of Software & Microelectronics, Peking University, Haidian, Beijing, China

Abstract: The use of artificial intelligence is becoming a reality in the educational field. The development of AI technology, in addition to demand and popularity of technological innovations, has given rise to a large number of AI-education companies, and e-learning is poised to enter the advanced stage. The activities in which it is beginning to be implemented are the assessment of users' achievement and provision of a live environment. In this study, a model of the willingness to continuously use AI-enabled language online e-learning products is constructed. The survey participants comprised users of online learning product in China and conclusions are drawn from the users' perspective through empirical analysis based on the unified theory of acceptance and use of technology, combined with perceived risk and perceived entertainment variables. Therefore, based on a theoretical framework, suggestions are provided to optimise the design and marketing of AI-enabled e-learning products and leverage the users' experiences to satisfy their needs. According to the results, from the perspective of users, we propose suggestions for the sustainable development and optimisation of AI-enabled online education products and strategies to help operators reconcile the experiences and needs of the users.

Keywords: e-learning; artificial intelligence; UTAUT model; adoption behaviour; language learning.

DOI: 10.1504/IJMLO.2022.119966

International Journal of Mobile Learning and Organisation, 2022 Vol.16 No.1, pp.74 - 94

Received: 07 Sep 2020
Accepted: 27 Dec 2020
Published online: 04 Jan 2022 *

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