How Artificial Intelligence Shapes Trust, Personalization, and Learner Engagement in Higher Education MOOCs: Evidence From a Developing Country
Despite the persistent challenge of low learner engagement in Massive Open Online Courses (MOOCs), particularly in Global South contexts, this study examines how artificial intelligence (AI)–enabled learning experiences shape multidimensional learner engagement in higher education MOOCs in Vietnam, based on an extended UTAUT2 framework. Specifically, it investigates the mediating role of behavioral intention in linking AI-enabled experiences to cognitive, emotional, and behavioral engagement.
Although MOOCs expand access to higher education, sustaining meaningful learner engagement remains a persistent challenge. Recent advances in artificial intelligence enable adaptive feedback, intelligent support, and personalized learning pathways; however, limited empirical research explains how these AI-enabled experiences translate into cognitive, emotional, and behavioral engagement, particularly in Global South settings such as Vietnam.
A sequential explanatory mixed-methods design was employed. Survey data from 652 MOOC learners in Vietnam (undergraduate students) were analyzed using structural equation modeling to test an integrated motivational–experiential framework. Semi-structured interviews with seven participants provided qualitative insights that explained the structural relationships and captured learners’ lived experiences with AI-supported learning.
This study develops an integrated framework to explain learner engagement in AI-enhanced MOOCs by extending the UTAUT2 model with AI-specific constructs, including trust in AI and perceived personalization. It reconceptualizes technology acceptance factors as functional learning motivations and socio-affective drivers in the context of AI-supported learning. Furthermore, it identifies behavioral intention as a central mediating mechanism linking AI-enabled experiences to multidimensional learner engagement.
AI-enabled learning experiences, together with functional learning motivations and socio-affective drivers, were significantly associated with learners’ behavioral intention, which functioned as the central mediating mechanism leading to behavioral, emotional, and cognitive engagement. Among the antecedents, trust in AI, perceived personalization, and hedonic motivation emerged as the strongest predictors of behavioral intention. Qualitative findings further revealed that learners experienced AI as an epistemic and affective partner that fostered metacognitive reflection, structured learning routines, and sustained motivation. However, the effectiveness of AI-enhanced MOOCs was conditioned by infrastructural limitations, uneven digital literacy, and socio-cultural learning contexts.
MOOC providers should design transparent, trustworthy, and personalized AI features that support self-regulated learning while ensuring low-bandwidth accessibility and contextual relevance for diverse learner populations.
Future studies should adopt longitudinal and cross-cultural designs and integrate learning analytics with self-reported engagement measures to capture the dynamic nature of learner–AI interaction.
By identifying mechanisms that foster sustained engagement in large-scale online learning, this study contributes to more inclusive and equitable access to higher education in developing countries.
Further research may examine experiential AI-supported learning interventions, participatory AI design with learners, and comparative analyses across Global South contexts.
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