Artificial Intelligence-Driven Personalized Learning Pathways in Vocational Education: Enhancing Competence, Engagement, and Outcomes
Year: 2025
Type: conference-paper
Study design: Observational Study
Abstract: Artificial intelligence-driven personalized learning pathways (AI-PLPs) leverage adaptive algorithms and learner analytics to deliver individualized educational experiences. Although earlier studies have acknowledged the value of these systems in fostering competence, maintaining learner engagement, and achieving positive outcomes, empirical investigations within vocational education settings remain limited. The present study explores the effects of AI-PLP quality on perceived competence, learning engagement, and learning outcomes in the context of Chinese higher vocational education, with particular attention to the moderating influence of technical support. Data were collected through a cross-sectional survey, resulting in 517 validated responses from students who had experience with AI-assisted learning. Structural equation modeling revealed that AI-PLP quality significantly enhanced competence, engagement, and outcomes. Competence further predicted both engagement and outcomes, and engagement positively influenced outcomes, confirming a competence–engagement–outcomes pathway. In contrast, the moderating effects of technical support were non-significant, suggesting that embedded system guidance may substitute for external assistance in mature AI ecosystems. These findings extend self-determination and constructivist learning theories to AI-mediated contexts and offer practical implications for those designing or governing vocational training systems incorporating AI.
Author: Jin Shi
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4416237883