We describe some key issues involved in building an intelligent tutoring system for the ill-defined domain of interpersonal and intercultural skill acquisition. We discuss the consideration of mixed-result actions (actions with pros and cons), categories of actions (e.g., required steps vs. rules of thumb), the role of narrative, and reflective tutoring, among other topics. We present these ideas in the context our work on an intelligent tutor for ELECT BiLAT, a game-based system to teach cultural awareness and negotiation skills for bilateral engagements. The tutor provides guidance in two forms: (1) as a coach that gives hints and feedback during an engagement with a virtual character, and (2) during an after-action review to help the learner reflect on their choices. Learner activities are mapped to learning objectives, which include whether the actions represent positive or negative evidence of learning. These underlie an expert model, student model, and models of coaching and reflective tutoring that support the learner. We describe several other cultural and interpersonal training systems that situate learners in goal-based social contexts that include interaction with virtual characters and automated guidance. Finally, our future work includes evaluations of learning, expansion of the coach and reflective tutoring strategies, and integration of deeper knowledge-based resources that capture more nuanced cultural aspects of interaction.