Validating the AI-assisted second language (L2) learning attitude scale for Chinese college students and its correlation with L2 proficiency

Abstract

The positive impact of Artificial Intelligence (AI) on second language (L2) learning is well-documented. An individual's attitude toward AI significantly influences its adoption. Despite this, no specific scale has been designed to measure this attitude, particularly in the Chinese context. To address this gap, our study aims to construct the AI-Assisted L2 Learning Attitude Scale for Chinese College Students (AL2AS-CCS) and evaluate its reliability, validity, and relationship with L2 proficiency. Our research comprises two phases, each involving separate samples. In Phase One (Sample 1: n = 379), we conducted exploratory factor analysis (EFA) to determine the factor structure of the AL2AS-CCS. The resulting two-factor structure consists of 12 items, categorized into cognitive and behavioral components. In Phase Two (Sample 2: n = 429), we performed confirmatory factor analysis (CFA) to validate the factor structure and assess model fit. CFA in Sample 2 confirmed the factor structure and demonstrated a good model fit. Additionally, the AL2AS-CCS exhibited high criterion validity, internal consistency, and cross-gender invariance. Our findings suggest that the AL2AS-CCS is a valid measurement tool for assessing Chinese college students' attitude toward AI-assisted L2 learning. Moreover, Chinese college students were discovered to maintain a moderately positive attitude toward AI-assisted L2 learning. Additionally, a positive correlation was identified between this attitude and their L2 proficiency.

Keywords

• Attitude toward AI-assisted L2 learning
• Scale construction
• Reliability
• Validity