Profiling Undergraduate EFL Learners’ Use of AI Tools: A Person-Centered Analysis of TAM and SRL
TL;DR
This study integrates the Technology Acceptance Model (TAM) and SRL to profile the use of AI tools by 365 Indonesian undergraduate EFL learners for independent learning by using a descriptive-correlational design with a person-centered approach.
Abstract
The effectiveness of AI tools in EFL learning depends not only on technology acceptance but also on learners’ self-regulated learning (SRL). However, prior research often treats learners as homogeneous and overlooks distinct user profiles. This study integrates the Technology Acceptance Model (TAM) and SRL to profile the use of AI tools (e.g., ChatGPT, Grammarly, and translation applications) by 365 Indonesian undergraduate EFL learners for independent learning. Using a descriptive-correlational design with a person-centered approach, data from a closed-ended Likert-scale questionnaire were analyzed through descriptive statistics, Pearson correlation, and two-stage cluster analysis (Ward's and K-Means). The results showed high TAM and SRL levels with significant correlations. Cluster analysis revealed three distinctive profiles: Highly Engaged (28.77%), Moderate (44.11%), and Low Engagement (27.12%). ANOVA confirmed significant differences (p < 0.001). Findings are context-bound and should not be generalized beyond the study setting, particularly across different educational and cultural contexts.