%0	Journal Article
%A	Dong, Hao
%A	Chen, Ximeng
%A	Shen, Jianglong
%C	China, Asia
%D	2026
%G	English
%J	International Journal of Educational Technology in Higher Education
%K	generative artificial intelligence
%K	learning analytics technology
%K	learning
engagement
%K	community of inquiry
%K	resource-based view
%K	digital literacy
%K	mixed-
methods research
%R	10.1186/s41239-026-00591-z
%T	Unpacking the impact mechanism of generative AI-based learning analytics technology on student learning engagement: the mediating role of community of inquiry and the moderating role of digital literacy
%U	https://link.springer.com/10.1186/s41239-026-00591-z
%V	23
%X	This study investigates how generative AI-based learning analytics technology (GLAT) influences university students’ learning engagement through the mediating role of the Community of Inquiry (CoI), and how digital literacy moderates this relationship. Grounded in the Resource-Based View (RBV) and CoI framework, the research explores the mechanism through which intelligent learning resources are transformed into learning behaviors, complementing classic learning theories with a unified “resource-capability-performance” analytical framework. Employing a mixed-methods design, data were collected through a two-phase survey (n = 400) and semi-structured interviews (n = 12). Partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to systematically examine the relationships among GLAT, the three dimensions of CoI (cognitive presence, social presence, and teaching presence), digital literacy, and learning engagement. The findings reveal that: (1) GLAT has a significant direct effect on learning engagement; (2) the three dimensions of CoI partially mediate the relationship between GLAT and learning engagement; (3) digital literacy positively moderates the effect of GLAT on each CoI dimension; (4) digital literacy positively moderates the direct effect of GLAT on learning engagement; and (5) fsQCA identifies four sufficient configurations leading to high learning engagement, highlighting a multifaceted mechanism encompassing technology, interaction, and capability. This study enriches the application of RBV and CoI in intelligent learning environments and offers theoretical and practical implications for enhancing GLAT design and student support strategies in higher education.
%@	2365-9440
%*	yes