Educational recommender systems have become a common phenomenon and are observed in daily life, from online course websites recommending courses to Google Scholar recommending research articles. Integration of context awareness can make recommendations more relevant to the user by utilizing user’s context information. Trust is a key contributor to the acceptance of context-aware recommender systems (CARS). Especially when the recommended item is used by learners for their knowledge and skills. In our investigation, we explore user feedback as a design intervention to help build trust in CARS. User feedback allows users to provide information to the system to improve in the future. In our study, we investigate the impact of user feedback on users’ Trust in CARS. We compare three variations of the level of user feedback: CARS without user feedback, with surface-level feedback (corrective feedback), and with in-depth feedback (suggestive feedback). We observed a near-significant difference in the level of user trust in CARS depending on the level of user feedback. We further discuss our results and its implications.