Building Explainable Recommender System for Engineering Students Work-Integrated Learning (WIL)

Work-integrated learning (WIL) is essential in engineering education, bridging the gap between theory and practice while enhancing students' employability. This study presents an LLM-enhanced, AI-driven hybrid content-based recommender system designed to optimize WIL experiences by tailoring recommendations based on students' academic and personal backgrounds, as well as the WIL tasks. A sample of 223 undergraduate engineering students participated, providing insights into their WIL experiences. Preliminary results show the internal performance of the recommender system with promising accuracy, and the system is further evaluated to understand its explainability and relevance.