Generalized Intelligent Framework For Tutoring Research Articles

Impact of Engaging with Intelligent Tutoring System Lessons Prior to Class Start

A study was conducted using the Generalized Intelligent Framework for Tutoring (GIFT) intelligent tutoring system (ITS) software and an Army Schoolhouse course. Existing materials were provided and used to create 9 computer-based GIFT lessons that included adaptive tutoring versions of the materials. These lessons included a Pre-Test, 7 content lessons (on the areas of Detect, Engage, and Assess), and a Post-Test. Six participants completed the Pre-Test, Post-Test, and all lessons. Overall Post-Test scores were significantly higher than Pre-Test scores, and remediation also had a positive impact on Post-Test scores. This study demonstrates that completing self-paced ITS lessons prior to initial class instruction can lead to positive learning impacts prior to the start of a class.

Utilizing the Generalized Intelligent Framework for Tutoring (GIFT) for Human Factors Education

Human Factors educators teach a variety of different types of classes including in-person, mixed mode, and online. The Generalized Intelligent Framework for Tutoring (GIFT) is a free, open-source domain-independent intelligent tutoring system (ITS) framework that can be leveraged by Human Factors educators for use in their own classes. GIFT includes authoring tools that are easy to use and do not require a background in computer science or ITSs. This demonstration is interactive and shows an overview of the tools and features of GIFT that can be used by Human Factors educators. It also provides context by showing a GIFT course in a Human Factors relevant area, and discusses how GIFT has and can be used in the classroom. As GIFT is open-source and continually being developed, the presenters provide the conference attendees an opportunity to give feedback about useful improvements that could be made to GIFT.

Creating a Team Tutor Using GIFT

With the movement in education towards collaborative learning, it is becoming more important that learners be able to work together in groups and teams. Intelligent tutoring systems (ITSs) have been used successfully to teach individuals, but so far only a few ITSs have been used for the purpose of training teams. This is due to the difficulty of creating such systems. An ITS for teams must be able to assess complex interactions between team members (team skills) as well as the way they interact with the system itself (task skills). Assessing team skills can be difficult because they contain social components such as communication and coordination that are not readily quantifiable. This article addresses these difficulties by developing a framework to guide the authoring process for team tutors. The framework is demonstrated using a case study about a particular team tutor that was developed using a military surveillance scenario for teams of two. The Generalized Intelligent Framework for Tutoring (GIFT) software provided the team tutoring infrastructure for this task. A new software architecture required to support the team tutor is described. This theoretical framework and the lessons learned from its implementation offer conceptual scaffolding for future authors of ITSs.

Modeling Expert Behavior in Support of an Adaptive Psychomotor Training Environment: a Marksmanship Use Case

The U.S. Army is interested in extending the application of intelligent tutoring systems (ITS) beyond cognitive problem spaces and into psychomotor skill domains. In this paper, we present a methodology and validation procedure for creating expert model representations in the domain of rifle marksmanship. GIFT (Generalized Intelligent Framework for Tutoring) was used as the architecture to guide development efforts and was paired with an Army marksmanship simulator that collects behavioral information through sensor technologies. The models were based on expert data from eight members of the U.S. Army Marksmanship Unit’s Service Rifle Team. The goal is to establish validated models that serve as artificial intelligence assessment criteria for driving a self-regulated training environment. We review the techniques applied to the data for model construction, the trends found in the data that are generalized across each expert informed through cross-fold validation practices, and discuss how the models will be used for driving real-time assessment. Results support the utility of generalized expert models across the fundamental components of rifle marksmanship as outlined in U.S. Army doctrine.

Designing Adaptive Instruction for Teams: a Meta-Analysis

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.

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

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.

Adaptive instruction for medical training in the psychomotor domain

The adaptive instruction provided by Intelligent Tutoring Systems (ITSs) tailors direction, support, and feedback to enhance/maintain the learning needs (e.g., lack of knowledge or skill) of each individual. Today, ITSs are generally developed to support desktop training applications, with the most common domains involving cognitive problem solving tasks (e.g., mathematics and physics). In recent years, implementations of game-based tutors authored using the Generalized Intelligent Framework for Tutoring (GIFT), an open-source tutoring architecture, provided tailored training experiences for military tasks through desktop applications (e.g., games including Virtual Battlespace and Virtual Medic). However, these game-based desktop tutors have also been limited to adaptive instruction for cognitive tasks (e.g., problem solving and decision-making). The military requires adaptive instruction to extend beyond the desktop to be compatible with the physical nature of many tasks performed by soldiers, sailors, and airmen. This article examines how commercial sensor technologies might be adapted to work with GIFT and support tailored computer-guided instruction in the psychomotor domain for a military medical training task, specifically hemorrhage control. Toward this goal, we evaluated the usability and system features of commercial smart glasses and pressure-sensing technologies. Smart glasses were selected as the focus of this study over handheld mobile devices in order to promote a hands-free experience during the training of hemorrhage-control tasks on a mannequin. Pressure sensors were selected to provide direct measures of effectiveness during the application of tourniquets and pressure bandages. Each set of technologies (smart glasses and pressure sensors) was evaluated not with respect to each other, but with respect to their capabilities to support adaptive instruction in the wild at the learner’s point-of-need and criteria based on established usability heuristics. Instruction in the wild is training provided in an environment outside the classroom and areas where tracking and sensing infrastructure are available (e.g., deployed areas of operation). We examined a wide range of features and capabilities, and evaluated their compatibility with the hemorrhage-control task, to answer the following question: what system design features (e.g., usability and interaction) are needed to support adaptive instruction for this individual psychomotor task at the point-of-need in locations where no formal training infrastructure is available?

To Embed or Not to Embed, that is the Question

In this paper we investigate intelligent tutoring communications in game-based learning platforms and examine its effect on perceived flow and immersion. The technology influencing this research is the open-source Generalized Intelligent Framework for Tutoring (GIFT), a domain-independent architecture used to author, deliver, and evaluate intelligent tutoring technologies. Influenced by theoretical underpinnings associated with flow, the resulting experiment looks at the application of Embodied Pedagogical Agents (EPA) in a game-based training environment used for communicating tutor generated feedback. Conditions were structured to examine tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone. Statistical analyses are presented examining treatment effects on an individual’s perceived sense of flow during interaction, along with examination of game characteristics required to support immersion. Outcomes support the use of an EPA present in GIFT’s TUI when compared to flow outcomes on audio prompts alone and the game embedded EPA.

The Challenges of Building Intelligent Tutoring Systems for Teams

Intelligent Tutoring Systems have been useful for individual instruction and training, but have not been widely created for teams, despite the widespread use of team training and learning in groups. This paper reviews two projects that developed team tutors: the Team Multiple Errands Task (TMET) and the Recon Task developed using the Generalized Intelligent Framework for Tutoring (GIFT). Specifically, this paper 1) analyzes why team tasks have significantly more complexity than an individual task, 2) describes the two team-based platforms for team research, and 3) explores the complexities of team tutor authoring. Results include a recommended process for authoring a team intelligent tutoring system based on our lessons learned that highlights the differences between tutors for individuals and team tutors.

Feedback Source Modality Effects in Game-Based Training

In this paper we explore available tools for integrating intelligent tutoring communications in game-based learning platforms and examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research is the open-source Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools to author, deliver, and evaluate intelligent tutoring technologies. Influenced by social cognitive theory and cognitive load theory based research, the resulting experiment looks at varying approaches for using an Embodied Pedagogical Agent (EPA) defined as a tutor in a game-based training environment. Treatments were authored to examine tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. A condition effectiveness evaluation is presented examining treatment effects on performance, reported mental demand, and perceived usefulness of feedback. Outcomes support the use of an EPA present in GIFT’s TUI when compared to effectiveness scores on audio prompts alone and the game embedded EPA.

Feedback source modality effects on training outcomes in a serious game: Pedagogical agents make a difference

The aim of this research is to enhance game-based training applications to support educational events in the absence of live instruction. The overarching purpose of the presented study was to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the open-source Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any instructional domain. Influenced by research surrounding social cognitive theory and cognitive load theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based training environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor–User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supported the application of using an EPA embedded in GIFT’s TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment.

The Generalized Intelligent Framework for Tutoring (GIFT) as a Tool for Human Factors Professionals

The Generalized Intelligent Framework for Tutoring (GIFT) is an open-source domain-independent intelligent tutoring system (ITS) framework. It provides the tools and capabilities to design a complete ITS, manage student instruction, and serve as a testbed for experimentation and analysis. GIFT was developed to provide a cost-effective and efficient means for developing ITSs that have interchangeable parts and can be reused. Additionally, GIFT provides the ability for human factors professionals to develop and run complete experiments in a computer-based environment. This demonstration will provide an introduction to GIFT, along with the motivation behind its design. Further, a demonstration of the tools that are useful to human factors practitioners and psychologists will be provided, and examples of courses that have been developed using GIFT will be shown.