interaction-analysis
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- PaperReCALL13 Jul 2026
Fostering generative AI-supported self-regulated learning (GenAI-SRL) in informal digital language learning through literacy and interactions: A two-stage PLS-SEM-ANN approach
Xiaoqi Wang, Lawrence Jun Zhang
A study of 343 Chinese university foreign language learners used PLS-SEM and ANN to examine how GenAI literacy and interactions influence self-regulated learning (SRL) in informal digital language learning. Awareness and evaluation dimensions of GenAI literacy predicted GenAI-SRL, while usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions facilitated GenAI-SRL, with student–student interaction as the strongest predictor, and awareness and evaluation partially mediated the effects of student–student and student–teacher interactions.
Original abstract
Generative artificial intelligence (GenAI) enables foreign language learners to extend their learning beyond formal instruction and develop their autonomy. However, research has not adequately examined how learners regulate their learning with GenAI or how their GenAI literacy and multiple types of interactions influence their self-regulated learning (SRL) in GenAI-supported informal digital language learning settings. We address this gap by analyzing data from 343 Chinese university foreign language learners through partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). PLS-SEM showed that awareness and evaluation significantly predicted GenAI-supported SRL (GenAI-SRL), whereas usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions emerged as facilitators of GenAI-SRL. These three interaction types also significantly influenced most GenAI literacy dimensions, with three of them predicting awareness, usage, and evaluation, while only student–student and student–GenAI interactions significantly predicted ethics. Mediation analysis demonstrated that awareness and evaluation partially mediated the effects of student–student and student–teacher interactions on GenAI-SRL. The mediating pathways through student–GenAI interaction were not significant. ANN models identified student–student interaction as the strongest predictor of GenAI-SRL. These findings inform GenAI literacy development and the design of systems to support GenAI-SRL in informal learning contexts.