“To Use or Not to Use?” A Mixed-Methods Study on the Determinants of EFL College Learners’ Behavioral Intention to Use AI in the Distributed Learning Context

• Published in: International review of research in open and distance learning Vol. 25; no. 3; pp. 158 - 178
• Main Authors: Wu, Hanwei, Wang, Yunsong, Wang, Yongliang
• Format: Journal Article
• Language: English
• Published: Athabasca Athabasca University Press (AU Press) 01.08.2024; International Review of Research in Open and Distance Learning; Athabasca University Press
• Subjects: Algorithms; Artificial intelligence; Attitudes; behavioral intention; Chatbots; College students; Communication Strategies; distributed learning; Education; EFL college learner; Emotional Intelligence; English (Second Language); English Language Learners; English Teachers; Essays; Feedback; Intention; Interviews; Language; Language Processing; Language Teachers; Literature Reviews; Machine learning; Mixed methods research; Natural language processing; Pedagogical Content Knowledge; Perceptions; Secondary School Students; Self Efficacy; Sentence Structure; Social Influences; Speaking; Speech Skills; Stakeholders; Student Motivation; Student writing; Teachers; Teaching; Teaching Methods; Technology adoption; Writing Instruction
• ISSN: 1492-3831; 1492-3831
• DOI: 10.19173/irrodl.v25i3.7708

Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.