Adaptive Learning in Vocational Education: AI-Powered Content Recommendations

Vocational education plays a critical role in equipping learners with practical skills aligned with industry demands. However, rapid technological advancements and evolving job requirements necessitate frequent updates to curriculum content. Traditional curriculum design methods often fail to address these dynamic needs, leading to mismatched learning experiences and inadequate skill development. This research focuses on developing an Intelligent Recommendation System (IRS) for vocational education curriculum content, leveraging Artificial Intelligence (AI) technologies to create personalized and industry-relevant learning pathways. The system utilizes a dataset comprising vocational curriculum content records (e.g., course titles, descriptions, skill categories, and industry relevance) and student profiles (e.g., learning preferences, academic performance, and career goals). Interaction logs are also incorporated to track user engagement with recommended courses and materials. The data undergo pre-processing, including tokenization and stop word removal, followed by feature extraction using Word2Vec to capture semantic relationships between terms. The core of the system is a Refined Gorilla Troop Optimized Deep Neural Network (RGTO-DNN) model, designed to enhance recommendation accuracy and efficiency. Experimental results demonstrate the effectiveness of the proposed system in improving recommendation accuracy and efficiency, with performance metrics showing high levels of accuracy (97.2%), precision (94%), recall (95.4%), and F1-score (96.3%). The IRS significantly enhances student engagement, learning outcomes, and curriculum design by providing a data-driven approach to modernizing vocational education. These findings highlight the potential of AI-driven recommendation systems to revolutionize vocational education by delivering a more personalized, efficient, and industry-relevant learning experience.