Abstract

To address the emerging trend of language learning with Artificial Intelligence (AI), this study explored junior and senior high school students’ behavioral intentions to use AI in second language (L2) learning, and the roles of related technological, social, and motivational factors. An eight-factor survey was constructed using a 5-point Likert scale. A total of 524 valid responses were collected, including 280 responses from junior high school students and 244 from senior high school students. The reliability and validity of the scale were satisfactory. The technological and social factors include effort expectancy, performance expectancy, social influence, facilitating conditions of AI-assisted language learning (AILL), which were hypothesized to predict students’ behavioral intention to use AILL with reference to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The motivational factors derived from L2 Motivational Self System theory (i.e. learning experience with AI, cultural interest with AI, and instrumentality-promotion with AI) were hypothesized to be intermediate variables between the technological and social factors and behavioral intention based on the extended UTAUT (UTAUT2). Therefore, UTAUT and the L2 Self System were combined according to UTAUT2 to construct the proposed model in this study, named AILL-Motivation-UTAUT model. The results of the structural equation models of AILL-Motivation-UTAUT showed that performance expectancy, cultural interest, and instrumentality-promotion could predict students’ behavioral intention to use AILL for both junior and senior high students; effort expectancy and social influence could predict behavioral intention to use AILL only for junior high students, learning experience with AI could predict behavioral intention to use AILL only for senior high students, while facilitating conditions could not predict behavioral intention to use AILL for either group. The predictive power (80% for senior high students and 74% for junior high students) of the AILL-Motivation-UTAUT model in this research is higher than or equal to that of UTAUT2 (74%). In addition, this study found that the technological and social factors perceived by students would predict the motivation in AILL. The model verified in this study may inform future studies on AI integration for English as foreign language learning.

摘要

为了应对人工智能（AI）语言学习的新兴趋势，本研究探讨了初中生和高中生在第二语言（L2）学习中使用人工智能的行为意图，以及相关技术、社会和动机因素的作用。使用 5 点李克特量表构建了八因素调查。共收集有效回复524份，其中初中生280份，高中生244份。该量表的信度和效度均令人满意。技术和社会因素包括预期努力、预期表现、社会影响、人工智能辅助语言学习（AILL）的便利条件、假设参考技术接受和使用统一理论 (UTAUT) 模型来预测学生使用 AILL 的行为意图。源于 L2 动机自我系统理论的动机因素（即人工智能的学习经验、人工智能的文化兴趣和人工智能的工具性促进）被假设为基于扩展 UTAUT 的技术和社会因素与行为意图之间的中间变量（ UTAUT2）。因此，根据UTAUT2，将UTAUT和L2自我系统结合起来，构建本研究提出的模型，命名为AILL-Motivation-UTAUT模型。AILL-Motivation-UTAUT 结构方程模型的结果表明，绩效期望、文化兴趣、工具性推广可以预测初中生和高中生使用AILL的行为意图；努力预期和社会影响力只能预测初中生使用AILL的行为意图，人工智能学习经验只能预测高中生使用AILL的行为意图，而便利条件不能预测任一群体使用AILL的行为意图。本研究中AILL-Motivation-UTAUT模型的预测能力（高中生为80%，初中生为74%）高于或等于UTAUT2（74%）。此外，这项研究发现，学生感知到的技术和社会因素可以预测 AILL 的动机。