Abstract
As AI technologies become increasingly integrated into students’ self-regulated learning practices globally, there is a growing need for pedagogical and research tools to assess students’ strategic use of those technologies. However, rigorously validated instruments measuring AI-integrated self-regulatory learning strategies are limited. Drawing upon existing empirical evidence about learner practices involving AI, this pioneering study developed an inventory of AI strategies focusing on performing pedagogical tasks, general language learning, and self-regulated used of AI respectively. Exploratory factor analyses and Rasch analyses based on data from the first questionnaire survey (n = 488) revealed four dimensions underlying the first scale (i.e., task processing, language compensation, product refinement, and evaluation & assessment), two dimensions comprising the second scale (i.e., vocabulary & grammar learning, and integrated language learning), and four dimensions underpinning the third scale (i.e., output regulation, tool regulation, knowledge regulation, and ethics regulation). These structures were further validated through confirmatory factor analyses using data from the second questionnaire survey (n = 707), with the subscales demonstrating good convergent validity, discriminant validity and reliability. Multi-group analyses also found those subscales generally showed measurement invariance across both the gender and disciplinary groups, supporting the stability of the identified structures across key demographics. Although developed within the English as a Foreign Language (EFL) context in China, the inventory is expected to have broad applicability in AI-integrated education and related research, including serving as diagnostic, instructional, and research instruments for students, teachers and researchers.
Presenters
Xiaohua LiuAssistant Professor, School of Humanities and Social Science, The Chinese University of Hong Kong (Shenzhen), China
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Artificial Intelligence, Self-regulated Learning, Learning Strategy, Instrument Development, Instrument Validation
