Shared Mental Models in Support of Adaptive Instruction for Teams Using the GIFT Tutoring Architecture

Abstract

Teams and teamwork are ubiquitous in military and civilian organizations. Their importance to organizational success cannot be overstated. This article describes the relationship and effect of three concepts: Intelligent Tutoring Systems (ITSs), shared mental models, and teamwork. The nexus between these concepts is examined to determine its capability to support adaptive instruction of teams, defined here as collectives of interdependent individuals who must communicate and interact with each other in order to perform assigned tasks and missions. An assumption underlying this examination is that augmenting the mental modeling processes of ITS with the mental models shared by members of interdependent teams will allow the considerable and increasingly research-established capabilities of intelligent tutoring of individuals to be applied in training teams. Specifically, we reviewed the learning and performance literature to identify how shared mental models of cognition could be used to enhance the adaptive instruction of teams. Our goal is to develop a methodology to enhance training and educational options for institutions that provide adaptive team instruction at the point-of-need. Toward this end, we discuss the adaptation of the Generalized Intelligent Framework for Tutoring (GIFT), an open source tutoring architecture, to accommodate team models and states. While this article makes a first step toward defining a process for team tutoring, challenges remain. Team tutors must have the ability to manage uncertainty and the dynamic nature of team interaction and communication in order to make effective and timely decisions that optimize team and team member performance.

Study on Extended Scratch-Build Concept Map to Enhance Students’ Understanding and Promote Quality of Knowledge Structure

“…The results of creating the original map show that each student could express their knowledge naturally, as indicated by the achievement of a varied number of propositions. Fletcher et al [37] argued that knowledge structure within groups illustrates variations of individual ideas according to their responsibility. Phase 2 is a crucial stage that illustrates the main activities of ESB concept mapping.…”

Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research

“…While GIFT has been used as an ITS authoring and experimental tool across a variety of task domains including more traditional topics in science, technology, education, and mathematics (STEM) (e.g., Renduchitnala and Matthews 2017;Warta 2017), this special issue focused on three distinctive topics: 1) sensitivity to learner affect (DeFalco et al 2017), adaptive training of psychomotor tasks (Goldberg et al 2017), and 3) adaptive instruction of teams (Fletcher and Sottilare 2017;Gilbert et al 2017;Sottilare et al 2017aSottilare et al , 2017b. Although each article describes a functional capability of significant importance to military training and education, we propose that the processes described therein could easily be transferred and applied to parallel civilian training and educational domains.…”

“…In this special issue, Fletcher and Sottilare (2017) reviewed the learning and performance literature to identify how shared mental models of cognition could be used to enhance the adaptive instruction of teams. The goal was to develop a methodology and extend GIFT to enhance adaptive team instruction at the point-of-need.…”

Sentiment analysis of Artificial Advisors in Search and Rescue Tasks

“…While most research focuses on one-to-one tutoring scenarios like issuing adaptive instructions to individual learners, growing attention is being paid to improving human teamwork with artificial advisors. Fletcher and Sottilare (2018) proposed the Generalized Intelligent Framework for Tutoring to provide adaptive instructions for teams based on shared mental models. Gupta and Woolley (2021) modeled human teamwork as a transactive systems framework and pointed out how AI could help with the emergence of collective memory, attention, and reasoning.…”

“…The complexity of modeling humans scales up when it involves teamwork. Even for cooperative tasks where the team shares the same goal, every team members have different observations, beliefs, and intentions (Fletcher & Sottilare, 2018). The social interaction between team members brings room for miscommunication, false memory, and second-order belief, where artificial agents could intervene and provide help.…”