Recommender systems (RS) are an efficient tool to reduce information overload when one has an overwhelming choice of resources. Embedding context-awareness into RS is found to increase accuracy and user satisfaction by allowing systems to consider users current situation (context). Context-aware recommender system (CARS) has applications in various areas, including education, where it can help learners by suggesting learning resources, peers to collaborate with, and more. When CARS is used in a learning context, it adds to the issue of lack of trust in the information, source, and intention as one builds knowledge through it. Further, embedding context-awareness adds to the trust issue due to the additional layer of automated context detection and context interpretation without users involvement. I investigate how to build trust in CARS in an educational setting. My investigation will be threefold (a) Understanding users perceptions of CARS; (b) Investigating design interventions to build trust in CARS; (c) Designing and evaluating a multidimensional approach to build trust in CARS.