A renaissance in the research and development of computer-based, tutoring systems over the last ten years is motivating scientists to ponder the application of intelligent tutors and coaches in more challenging team training problem spaces where human tutors are either unavailable or impractical. This paper reviews some of the challenges and emerging technologies (tools and methods) that might influence the development of adaptive, intelligent tutors for geographically-distributed team training. Team tutoring presents many challenges. Even human tutors struggle to develop team cohesion, coordinate roles and responsibilities of team members and assess their contributions (Sottilare, 2010). Computer-based tutors face additional challenges: sensing and assessing the cognitive state (including affect) of each team member (Sottilare and Proctor, in press; D'Mello and Graesser, 2007) in near real-time to understand each team member's readiness to learn (e.g., their engagement and motivation); measuring team performance; perceiving and weighing team member contributions to team performance; and selecting instructional strategies that will optimize team performance. Emerging sensing technologies are showing promise as enablers of computer-based perception of each team member's behavior and physiology with the goal of predicting unobserved variables (e.g., cognitive state). Along with performance measures, historical and self-reported data, behavioral and physiological measures can provide the tutor with the information needed to model the trainee's state and their relationship with other team members and the tutor. Accurate (and timely) trainee and team state information (e.g., performance, competency, trust) are considered to be determining factors for the team tutor to select appropriate instructional strategies (e.g., support, direction) for optimal team performance. Design goals, ongoing experimentation, and potential applications of computer-based team tutors are also discussed.