Abstract
The authors proposed a web-based AI framework that dynamically generates personalized learning paths for vocational learners. By integrating deep knowledge tracing, semantic path encoding, and multi-objective reinforcement learning, the system balanced completion efficiency, satisfaction, and dropout risk in real time. A/B tests across three vocational domains showed 8–14% gains in completion and 0.7–1.2-point boosts in satisfaction over baseline. The approach is deployable, interpretable, and adaptable to resource-constrained settings.Article Preview
TopSummarize
The integration of artificial intelligence (AI) into vocational education has become increasingly vital as the global economy evolves towards digitalization and cross-domain integration. Recent studies have highlighted the potential of AI to optimize personalized learning pathways, addressing the dynamic nature of skill demands and the need for adaptive educational systems.
Deng et al. (2024) explored strategies for optimizing personalized learning pathways using AI assistance, emphasizing the importance of real-time adaptation and learner engagement. Their work underscored the necessity for vocational educational systems to dynamically adjust to changing industry requirements, ensuring that learning paths remain relevant and effective. This approach aligns with the broader trend of leveraging AI to enhance educational outcomes by providing tailored learning experiences that meet individual needs.
Collectively, these studies highlight the growing role of AI in vocational education, emphasizing the need for adaptive, personalized learning systems that can respond to the dynamic demands of the modern workforce. The integration of AI technologies not only enhances the relevance and effectiveness of learning paths but also supports the broader goals of vocational education, including skill development, learner engagement, and adaptability to industry changes.