Individualized Instruction.
We can see instantiation of the start of this revolution for training already occurring in many domains. In education, evidence-based instructional strategies, curriculums, and training tools trend toward individualized and adaptive methodologies to capitalize on individual student strengths and weaknesses. For example, universal design for learning (UDL) is centered on applying individualized technology to facilitate learning, addressing the disconnect between an increasingly diverse student population and “one-size-fits-all” curriculums (Rose and Meyer 2002). Similarly, flipping the classroom attempts to make coursework more compatible with varied learning styles by providing a variety of tools (often technology-based) to gain first exposure to material outside of class, using class time to assimilate knowledge through interaction-based methods such as problem-solving, discussion, or debates. Numerous studies have demonstrated significant learning gains using this approach (Hake 1998; Mazur 2009; DesLauriers et al. 2011).
The application of individualized, adaptive instructional methodologies using agent-based technology has also been demonstrated (VanLehn 2011). Intelligent Tutoring Systems (ITS) monitor individual user interactions and states, and use artificial intelligence tools to assess trainee performance adaptively, applying pedagogical interventions to support learning (Goldberg et al. 2012). The concept of intelligent tutoring systems for teams has also been established (Sottilare et al. 2017), with scientific gaps identified (Goodwin et al. 2015) and establishment of research efforts focused on advancements needed for team-based ITS. Advancements in both individual and team-focused ITS, coupled with advancements in continuous measurement and understanding of human states and behaviors, create a critical path forward for a training revolution that incorporates individualized, adaptive instruction. While much of the focus now is on individual learners and individual development, initial work has begun to consider the use of individualized instruction for enhancing teamwork.
Team-focused Training.
As research on individualized, adaptive instruction progresses, a body of work on team-focused training provides some recommendations for training humans to work better in teams. Coherent and overlapping of theories and models of team effectiveness have emerged in the literature, which define a critical set of team states and processes for enhanced performance and effectiveness in complex, dynamic groups (Marks et al. 2001; Ilgen et al. 2005; Salas et al. 2005; Burke et al. 2006). Additionally, teamwork competencies have been defined (Cannon-Bowers et al. 1995; Cannon-Bowers and Salas 1997; Salas et al. 2009) and training programs have been built around these competencies (e.g. Shuffler et al. 2010). Much of the team-based training literature focuses on getting teams up to speed, ready to perform together quickly (e.g. Horn, 2014), which is critical to the future notion of more diverse, rotating team members. The effectiveness of training for teamwork has been reliably demonstrated for both teamwork behaviors and overall team performance (see (McEwan et al. 2017), for review), with the most effective team training programs focused on teamwork as opposed to taskwork (Salas et al. 2008). However, this work has primarily been focused on human teams or use of technology as a tool (rather than a teammate) and generally does not conceptualize of agents as team members who facilitate continuous team learning, enhanced processes, and adaptation.
Realistic Training Environments.
Finally, research findings in training design, coupled with recent advancements in technologies, have led to a focus on realistic training environments, on-the-job training, and training at the point of need within industry, academia, and the military. In a summary of the literature on realistic training environments, Grossman and Salas (2011) conclude that conducting training and practice in realistic training environments, or environments that resemble the workplace, increases the likelihood that trained competencies will transfer to knowledge and performance on the job. McEwan et al. (2017) reported similar findings with use of realistic training environments and simulations for team performance. Recent technological advancements, including the proliferation and cost-effectiveness of virtual and augmented reality systems, coupled with artificial intelligence for enhanced experience and customization, create the potential for large-scale implementation of this on-the-fly training in realistic settings, thereby adding to the potential for a revolution in training.
Call for Comments:
- What other advancements will enable training for future human-agent teaming?
- What barriers will prevent training for future human-agent teaming?
- Other related comments
