The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams.
While one-to-one human tutoring has claimed to be significantly more effective than one-to-many instructional methods (e.g., traditional classroom instruction; Bloom 1984; VanLehn 2011), it is neither a practical nor affordable solution in large organizations (e.g., academic, corporate, or military; Sottilare and Proctor 2012). The use of computer-based tutoring programs for learning has seen a renewed interest in training and educational domains and one-to-one computer-based tutoring continues to emerge as a practical alternative to one-to-one human tutoring. One-to-one tutoring via Intelligent Tutoring Systems (ITSs) provides tailored experiences to engage individual learners, offers an effective means to enhance their learning and performance, but have focused mainly on well-defined educational domains (e.g., cognitive tasks involving problem solving or decision-making). Tutors for physics, mathematics, and software programming make up the bulk of the ITSs produced today. A recent review of artificial intelligence in education (AIED) metaanalyses by du Boulay (2016) noted investigations by VanLehn (2011), Ma et al. (2014), Kulik and Fletcher (2015), Steenbergen-Hu and Cooper (2013, 2014), and Pane et al. (2014). Each meta-analysis provided a range of results for effectiveness in the context of one-to-one tutoring in individual instructional domains.