The effects of perceived technical explanation and analogical explanation on learning behavioral intention among K-12 students with different subject preferences in AI-assisted learning.

AI tool use, self-efficacy, foreign language enjoyment, and willingness to communicate: a moderated serial mediation model among Chinese EFL learners

A moderated serial mediation model in which AI tool use predicts willingness to communicate sequentially through AI self-efficacy and foreign language enjoyment (FLE) is proposed and tested, with foreign language anxiety (FLA) moderating the FLE-to-WTC link.

AI-enhanced student conceptions of feedback: A PLS-SEM model of self-efficacy and self-regulation in EFL writing

Feedback is a core mechanism of formative assessment, yet its effect on learning depends on how students conceive of and act upon it. This study tests an AI-enhanced student conceptions of feedback model in English as a Foreign Language (EFL) writing, relating six feedback conceptions to academic self-efficacy and self-regulation. Survey responses from 300 undergraduate students at a state university in Indonesia were analysed with partial least squares structural equation modelling (PLS-SEM) using 5,000 bootstrap subsamples. The measurement model met every reliability and validity criterion: outer loadings ranged from 0.743 to 0.888, composite reliability from 0.857 to 0.931 and average variance extracted from 0.609 to 0.750, while the Fornell-Larcker criterion, cross-loadings and HTMT ratios all confirmed discriminant validity. All seven structural paths were significant. Active use of AI feedback was the strongest antecedent of self-efficacy, followed by enjoyment, whereas ignoring feedback exerted a significant negative effect. Self-efficacy in turn strongly predicted self-regulation and accounted for 43.5% of its variance. The results indicate that what students do with feedback matters more than the source from which it originates, and that self-efficacy is the pivotal mechanism translating feedback experience into self-regulated learning.

Perceived Usefulness, Perceived Ease of Use, and Self-‎Reported Critical Thinking Among Indonesian Vocational ‎Students: The Indirect Role of Self-Regulated Learning

The spread of generative artificial intelligence (AI) in vocational classrooms has raised a concern that easy, on-demand answers may displace the reasoning vocational graduates are expected to master. This cross-sectional survey examined whether students' perceptions of AI are associated with their self-reported critical thinking, and whether self-regulated learning accounts for any such association. Drawing on social cognitive theory and the Technology Acceptance Model, questionnaire data were collected from 365 students in nine state vocational schools (SMK) in Pesisir Selatan Regency, West Sumatra, and analysed with PLS-SEM. All four constructs, including critical thinking, were measured by self-report rather than by performance tasks. Perceived usefulness and perceived ease of use were not associated with critical thinking directly (β = -.026, 95% CI [-.163, .111]; β = .064, 95% CI [-.072, .200]) but were weakly associated with self-regulated learning (β = .161; β = .158), which was in turn the strongest correlate of critical thinking (β = .383, 95% CI [.284, .482]). Both indirect paths were small and positive (β = .062 and .061), a configuration indicating indirect-only mediation. Explanatory power was weak, with R² = .085 for self-regulated learning and .157 for critical thinking, and f² values for all four technology paths below .02. A clustering sensitivity analysis showed that the technology paths lose significance at an intraclass correlation above about .01, so these associations are provisional. Because all constructs were measured on one occasion, the ordering is a theoretical assumption rather than an observed sequence.

Short-video use and university students’ creativity in higher education: the roles of cognitive elaboration, absorptive capacity, and anticipated regret

Short-video platforms are increasingly embedded in university students’ everyday learning environments, but their implications for creativity remain contested. Drawing on a cognitive-affective self-regulation perspective, this study examines whether short-video use (SVU) is associated with university students’ creativity (CRE), whether cognitive elaboration (CE) and absorptive capacity (AC) explain this association, and whether anticipated regret (AR) changes these cognitive pathways. Survey data from 502 university students in China were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. The analysis emphasized prediction-oriented model assessment, including reliability, discriminant validity, f 2 effect sizes, adjusted R 2 , model fit, and predictive relevance checks. The results show that SVU is positively associated with CRE and is also positively related to CE and AC, both of which positively predict CRE. Both indirect effects are significant, with the CE pathway numerically stronger than the AC pathway, although the difference between the two indirect effects is not statistically conclusive. AR strengthens the SVU-CE relationship but does not significantly moderate the SVU-AC relationship. Gender-based MGA provides tentative evidence that the AR-based strengthening of the SVU-CE pathway is more visible among female students than among male students. These findings suggest that SVU should not be treated only as a disruptive digital habit in higher education. When accompanied by reflective processing and knowledge integration, short-video engagement may provide stimuli for idea generation and creative learning.

How Cognitive Factors and Motivation Shape Learners' Engagement in AI ‐Supported Language Learning?: Insights From the Combined SEM and fsQCA Approaches

These findings provide theoretical support for SCT in technology‐mediated learning and suggest practical strategies for educators, including tailoring instructional design to learners' cognitive profiles, fostering motivation and self‐efficacy and leveraging AI tools to enhance active and sustained engagement in language learning.

Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors.