ARL-SR-0318 ● MAR 2015

US Army Research Laboratory

Fundamentals of Adaptive Intelligent Tutoring 

Systems for Self-Regulated Learning

by Robert A Sottilare

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ARL-SR-0318 ● MAR 2015

US Army Research Laboratory

Fundamentals of Adaptive Intelligent Tutoring 

Systems for Self-Regulated Learning

by Robert A Sottilare

Human Research and Engineering Directorate, ARL

Approved for public release; distribution is unlimited.
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14. ABSTRACT

The tutorial described in this report was conducted at the Interservice/Industry Training Simulation and Education Conference 

(IITSEC) in Orlando, FL, in December 2014. The purpose of this tutorial is 5-fold: 1) understand the differences between 

adaptive and adaptable systems; 2) understand the key components of Intelligent Tutoring Systems (ITSs); 3) understand the 

potential of ITSs as one-to-one tutors and where ITS technologies are most applicable in the training and educational domain; 

4) understand the concept of self-regulated learning (SRL); and 5) understand how ITS design can support SRL.

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adaptive tutoring, intelligent tutoring systems, self-regulated learning, authoring adaptive systems, adaptive instruction,

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Robert A Sottilare

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Contents

List of Figures iv

1. Tutorial Objectives 1

2. Questions and Answers about Adaptive Tutoring 1

2.1 Question 1: What Is an Intelligent Tutoring System? 1

2.2 Question 2: How Are Intelligent Tutoring Systems Different from 

Computer-Based Training Systems? 1

2.3 Question 3: What Is an Adaptive System and How Is It Different from an 

Adaptable System? 2

2.4 Question 4: What Is Self-Regulated Learning? 4

3. Characteristics of Intelligent Tutoring Systems 4

4. Motivation for Using and Improving Intelligent Tutoring Systems 10

5. ITS Design in Support of Self-Regulated Learning 11

5.1 Learner Modeling in Support of Self-Regulated Learning 12

5.2 Instructional Management in Support of Self-Regulated Learning 13

5.3 Domain Modeling in Support of Self-Regulated Learning 15

5.4 Authoring in Support of Self-Regulated Learning 18

Bibliography 21

Distribution List 27
iv

List of Figures

Fig. 1 Merrill’s Component Display Theory.......................................................3

Fig. 2 Sottilare’s Learning Effect Model ............................................................4

Fig. 3 Adaptive training interaction between learner, training environment, and 

tutor...........................................................................................................5

Fig. 4 Composite ITS interface ...........................................................................6

Fig. 5 Data flow in ITSs......................................................................................6

Fig. 6 The Generalized Intelligent Framework for Tutoring (GIFT)..................8

Fig. 7 Examples of dialogue-based tutors: AutoTutor and AutoTutor Lite ........8

Fig. 8 What are adaptive ITSs good at training?.................................................9

Fig. 9 Motivation for adaptive tutoring systems: they work.............................11

Fig. 10 Sottilare’s Learning Effect Model ..........................................................11

Fig. 11 Application of Person’s (1995) 5-step tutoring process .........................14

Fig. 12 Dimensions of domain modeling for military training ...........................16

Fig. 13 Static or desktop interaction with adaptive tutors...................................17

Fig. 14 Limited kinetic interaction with adaptive tutors.....................................17

Fig. 15 Enhanced kinetic interaction with adaptive tutors..................................17

Fig. 16 Adaptive tutoring in the wild ..................................................................18

List of Tables

Table Low-cost behavioral and physiological sensors .....................................12
1

1. Tutorial Objectives

The purpose of this tutorial is 5-fold:

 Understand the differences between adaptive and adaptable systems

 Understand the key components of Intelligent Tutoring Systems (ITSs)

 Understand the potential of ITSs as one-to-one tutors and where ITS 

technologies are most applicable in the training and educational domain

 Understand the concept of self-regulated learning (SRL)

 Understand how ITS design can support SRL 

2. Questions and Answers about Adaptive Tutoring

To set the stage for subsequent elements of this presentation and to level-set 

knowledge within the audience, we present 4 key questions about adaptive 

tutoring and their corresponding answers. 

2.1 Question 1: What Is an Intelligent Tutoring System?

An ITS is a computer system that aims to provide immediate and customized 

instruction or feedback to learners, usually without intervention from a human 

teacher (Psotka and Mutter 1988). Koedinger and Tanner (2013) also describe an 

intelligent tutoring system as computer software designed to simulate a human 

tutor’s behavior and guidance. ITSs may also be defined as computer-based 

instructional systems with models of instructional content that specify what to 

teach and teaching strategies that specify how to teach (Murray 1999; Wenger 

1987; Ohlsson 1987).

2.2 Question 2: How Are Intelligent Tutoring Systems Different 

from Computer-Based Training Systems?

Computer-Based Training (CBT) systems are software-based and use computers 

to deliver instruction. CBT is also known as Technology-Based Training, 

Computer-Based Instruction, Computer-Assisted Instruction, or Computer-Based

Learning.

ITSs are a subset of CBT. CBTs deliver instruction consistently to all learners,

whereas ITSs are “intelligent”. This implies their ability to adapt or tailor 

instruction in real-time based on triggers, which are usually defined as policies 
2

sometimes called “production rules”. Triggers are usually changes to the learner’s

state(s) or the training environment. Policies are used by the tutor to recognize

changes and learning opportunities (e.g., teachable moments) and trigger actions 

by the tutor.

2.3 Question 3: What Is an Adaptive System and How Is It

Different from an Adaptable System?

Adaptable systems may be changed by the user. Flexible control of information or 

system performance automation resides in the hands of the user (Oppermann

1994). A smartphone user interface is adaptable and may be configured to support 

the specific educational or entertainment needs of the user.

Adaptive systems change behaviors based on observations of changing conditions 

in the user and/or the environment. In adaptive training systems, the agents 

observe and interpret each learner’s data (behaviors and physiology) to determine 

learner states (e.g., engagement, emotions, performance) and identify individually 

tailored learning needs. They respond to the learner’s states and needs by 

adjusting the challenge level of scenarios and amount/type of tutor support in near 

real time to maximize training effectiveness (e.g., performance, learning, 

retention, and transfer).

System change is usually managed by software-based agents who use artificial 

intelligence techniques to guide their decisions and actions. Software-based 

agents vary in their reactivity, proactivity, and cooperation. Examples of artificial 

intelligence techniques for managing adaptive training policies include the 

following:

 Production Rules 

 Decision Trees 

 Neural Networks

 Bayesian Networks

 Reinforcement Learning Algorithms

 Markov Decision Processes

Reactive agents respond to changes in the training environment or the learner and 

are active in assessing conditions related to policies that they are assigned to 

enforce. Proactive agents take initiative to achieve long-term goals and recognize 

opportunities (e.g., teachable moments). Proactive agents also learn and adapt 
3

through experience. Finally, cooperative agents share information and act together 

to achieve long-term goals.

Both adaptable and adaptive systems have the flexibility to change in the face of 

changing conditions, but adaptive systems have advantages in being able to 

offload control tasks to agents (Miller et al. 2005) resulting in

 greater speed of performance,

 reduced human workload, more consistency, and

 a greater range of flexibility in behaviors. 

An example of an adaptive tutoring method based on a learning theory is

Component Display Theory (CDT; Merrill et al. 1992). As shown below, CDT 

asserts that the optimal method to effective learning is as follows: 

 Gain attention and motivate.

 Adapt to the learner’s prior knowledge.

 Adapt to the type of knowledge being presented.

 Adapt to learner attributes.

 Adapt to the learner’s ability (intelligence, emotional intelligence, 

adaptability).

CDT specifies 4 quadrants for progressive stages of learning: rules, examples, 

recall, and practice (Fig. 1). Rules provide the basic principles needed to learn in a 

particular domain. Examples provide successful models of how to do a specific 

task in the domain. Recall provides assessments of the learner’s ability to 

recollect facts and methods from the rules and examples quadrants. Finally, the 

practice quadrant provides opportunities for the learner to apply knowledge and 

skills learned and reinforced in previous quadrants. Each of these quadrants 

requires that the tutor recognize the learner’s progress and states that moderate 

learning (e.g., emotion, workload, engagement). This recognition of changes in 

the learner is managed by adaptive agents.

Fig. 1 Merrill’s Component Display Theory
4

Another adaptive tutoring method is based on the adaptive tutoring Learning 

Effect Model (LEM; Sottilare 2012; Fletcher and Sottilare 2013; Sottilare 2013; 

Sottilare et al. 2013) as shown in Fig. 2. LEM, a learner-centric model, is largely 

domain independent. As such, LEM can be applied in a variety of domains with a 

variety of learners. LEM is the basis for the Generalized Intelligent Framework 

for Tutoring (GIFT; Sottilare et al. 2012), a modular architecture for 

automatically managing instruction. 

Fig. 2 Sottilare’s Learning Effect Model

2.4 Question 4: What Is Self-Regulated Learning?

To discuss SRL, we must first define self-regulation, which is controlling, 

adjusting, or conforming to standards without intervention from external entities. 

SRL is learning managed by the learner and guided by the processes of 

metacognition and motivation. Metacognition is thinking about how one thinks 

and learns (e.g., reflection about the learning process). Motivation is the 

importance or enthusiasm to take actions needed to learn and is closely tied to 

individual goals and values. ITSs can influence the success of SRL by 

influencing/reinforcing metacognition and motivation through scaffolding/

support.

3. Characteristics of Intelligent Tutoring Systems

In this section we will explore how ITSs function, what they look like, their scope 

of use, and their ideal characteristics. ITSs are adaptive systems that may be 

designed to recognize and adapt to a variety of changing conditions in both the 

learner and the environment (see Fig. 3).
5

Fig. 3 Adaptive training interaction between learner, training environment, and tutor

To support interaction between the learner and the logic of the ITS, ITSs have a 

variety of interface controls and information delivery windows that make up a 

tutor-user interface, or TUI (see Fig. 4). Typical functions for TUIs for ITSs 

include the following:

 Content presentation window. This window provides multimedia 

content in support of learning objectives.

 Tutor natural language feedback window. This window provides a 

human representation to converse with the learner and provides questions, 

hints, prompts, direction, support, and other feedback to the learner 

verbally. Natural language responses from the learner are converted to text

and analyzed by the ITS. Then, an appropriate response is selected, and 

natural language is generated and delivered by the virtual human.

 Tutor text feedback window. This window offers the option of providing 

text feedback in the absence of a natural language interface or in addition 

to a natural language interface. 

 Learner response window. This window provides the learner with the 

option of typing responses to questions or other queries by the ITS.

 Conversation log. This window tracks conversation between the learner 

and the ITS so the learner can refer to it at a later date; this information 

might also be used by the tutor to support non-real-time feedback (e.g., 

after-action review).
6

Fig. 4 Composite ITS interface

So now that we know what ITSs look like, what their interface functions are, and that 

they interact with both the learner and the training environment, let us discuss how 

they work in real time. Nearly every tutoring system has 4 fundamental elements: a 

learner model, a pedagogical (instructional) model, a domain model, and a 

communication model. Figure 5 shows a functional diagram of the information flow 

between various modules in a tutoring system. This flow diagram is modeled after the 

information flow in GIFT and, specifically, the LEM discussed previously.

Fig. 5 Data flow in ITSs
7

The green boxes above show these fundamental elements as modules vs. models 

because they manage processes in addition to modeling the learner, the 

instruction, the domain, and the communication to/from the learner. The TUI has 

been discussed in detail, so now the learner, pedagogical, and domain modules 

will be reviewed:

 Learner module. In addition to receiving learner performance states (at, 

below, or above expectation) from the domain module, the learner module 

also uses real-time behavioral and physiological data along with historical

(e.g., progress toward objectives) and demographic data to classify learner 

cognitive, affective, physical, and shared states, which are provided to the 

pedagogical module.

 Pedagogical module. The pedagogical module models the instructional 

techniques (policies) and strategies (plans), and uses these to develop 

recommendations for execution by the domain module.

 Domain module. The domain module models the content, expert 

behaviors, measures of learning, and performance, and uses these to assess 

learner progress against expectations; it combines this information with 

strategy recommendations from the pedagogical module to select a tactic 

or action that provides media or interaction data to the TUI for display or 

natural language generation. 

There are various types of tutors, but they can be generally grouped into 2 

primary categories based on their underlying instructional models: 

 Example-tracing tutors (also called ray-tracing or model-tracing tutors) are 

fixed in how they instruct, but they can be created without programming,

and they require problem-specific authoring.

 Cognitive tutors are more flexible and adaptive but required longer 

development time because of the requirement to build a cognitive model 

of the learner; they support tutoring across a range of similar problems.

Some tutors may be classified as “shell” tutors. In other words, they are templates 

or frameworks to support the development of tutors in a variety of domains. One 

shell tutor is GIFT (Fig. 6)—a free, open-source tutoring architecture to 

 capture best tutoring practices; support rapid authoring, reuse, and 

interoperability of ITSs;

 lower costs and entry skills needed to author ITSs; and

 enhance the adaptiveness of ITSs to support SRL.
8

Fig. 6 The Generalized Intelligent Framework for Tutoring (GIFT)

Another class of tutors is “dialogue-based tutors”, which provide for Socratic 

interaction between the learner and tutor. An example of a dialogue-based tutor is 

AutoTutor, shown in Fig. 7.

Fig. 7 Examples of dialogue-based tutors: AutoTutor and AutoTutor Lite

Now that we’ve identified various types of ITSs, let us list the salient 

characteristics we would like to see in adaptive tutoring systems. Sottilare and 

Gilbert (2011) identified several ideal characteristics in their “platinum level 

tutor” for Army training:

 Self-regulated: Support learning of individuals and teams.

 Adaptive: Use artificial intelligence to tailor instruction to the learning 

needs of individuals and teams.

 Effective and credible: As good or better than an expert human tutor.

 Relevant: Support military training in both ill-defined and well-defined 

environments.
9

 Accurate and valid: Use optimal instructional methods based on empirical 

results.

 Usable: Tailored to different users (learners, authors, researchers, etc.).

 Accessible: Service-oriented and available anywhere 24/7/365.

 Affordable: Easy to author and promotes standards and reuse.

 Persistent: Models the learning needs of Warfighters across their careers.

Now that we have reviewed what ITSs are, how they work, and what we want 

them to be, let us identify the domains they are good at training. In Fig. 8, we 

have identified what we believe to be an optimal domain for using ITSs. 

Fig. 8 What are adaptive ITSs good at training?

Specifically, we have plotted cognitive learning objectives (x axis) identified by 

Bloom et al. (1956) in their cognitive taxonomy against the complexity of the 

learning content (y axis) to be presented to the user. Based on the time and skills 

needed to author an ITS, we envision the best application of this technology is in 

complex domains with complex learning content and high learner throughput. 

However, we also recognize that technologies are being developed to lower the 

bar for the skills and time it takes to author ITSs. In the future, ITSs must be 

suitable for teaching across all levels of domain complexity—even in courses 

where there is low throughput.
10

In light of this pending revolution, we recommend using ITSs for teaching tasks 

across the following domains: 

 Cognitive (e.g., complex decision making, strategic thinking, problem 

solving) 

 Affective (e.g., interpersonal skills, ethical conduct)

 Psychomotor (e.g., operating sophisticated weapons/platforms)

 Social (e.g., collaborative and team tasks) 

We also recommend using ITSs to prepare for live training/practice; to enhance 

learning within virtual training environments; and to support intelligent decisionaiding/mentoring on the job. The motivation for making the investment to use and 

improve ITSs is discussed in the next section.

4. Motivation for Using and Improving Intelligent Tutoring 

Systems

Today, ITSs have been primarily limited to well-defined domains, such as 

physics, mathematics, chemistry, and software development. ITSs are expensive 

and require specialized skills to author them. They are insufficiently adaptive to 

support highly effective, tailored training experiences across the broad spectrum 

of tasks conducted by our military today. Given the limited set of applications and 

functional limitations, what is our motivation for using and improving ITSs? 

First, there is a need. A smaller force requires each Warfighter to have expertise 

in a greater range of skills for complex missions. We need to achieve expertise 

faster with fewer resources. To do this we will need to tailor training and take 

advantage of what learners already know. Second, there is an opportunity to 

accelerate the development of expertise by developing ITSs as effective as expert 

human tutors, with cost-effective features. Finally, ITSs have been shown to be

highly effective (Fig. 9), so the promise of gold is there if we dig a little.
11

Fig. 9 Motivation for adaptive tutoring systems: they work

5. ITS Design in Support of Self-Regulated Learning

Since one of our goals is to support SRL, how can we design ITSs to support 

SRL? Four areas of opportunity are influencing the design of learner models, 

instructional models, domain models, and user interfaces. Given the time for this 

tutorial, we will focus on the first 3 in this report and leave the discussion of user 

interfaces for another time.

By understanding the relationship between learner data, learner states, 

instructional strategies, context and tactics, and learning gains (e.g., performance, 

learning, retention, and transfer) as noted in the LEM (Fig. 10), we can influence 

the process of SRL.

Fig. 10 Sottilare’s Learning Effect Model
12

5.1 Learner Modeling in Support of Self-Regulated Learning

In this section we will discuss the relationship between learner data acquisition 

and learner state classification. Understanding the state of the learner is critical to 

successful tutoring. Successful human tutors are experts at recognizing telltale 

signs in their students that infer whether their students are learning or not. To 

support SRL, computer-based ITSs must also be able to interpret learner data to 

classify learner states (e.g., confusion, frustration, boredom), which moderate 

learning gains.

Sensors are one method of acquiring data about the learner. One approach is to 

investigate low-cost sensor technologies to inform classification of key 

influencers of learning. The results of a survey of low-cost behavioral and 

physiological sensors (Carroll et al. 2011) are shown in the Table below, along 

with the states that they are best at classifying and their cost.

Table Low-cost behavioral and physiological sensors

States Sensor

Cost

($)

Anger/Frustration, Boredom Motion detector ~100

Anger/Frustration, Fear/Anxiety, Boredom Heart rate monitor ~100

Anger/Frustration, Boredom Chair pressure sensors ~200

Engagement Chair pressure sensors ~200

Attention, Engagement, Workload EEG ~200

Attention, Workload Eye tracker ~500

Other methods of acquiring data include surveys and assessments, and reading 

data from accessible databases (e.g., personnel records, learning management 

systems). Learner data may be labeled (supervised), unlabeled (unsupervised), or 

semi-supervised. Once you have learner data, this can be used to interpret various 

learner states using a variety of machine learning techniques.

Since training is a real-time process, we want to provide feedback to the learner in 

close time proximity to the event of interest. We want learner state classification 

processes that are also real time to maintain the close coupling of interaction 

between the learner and the tutor. Along with motivation, some of the key learner 

states are moderators or influencers of learning are cognitive (e.g., attention, 
13

engagement, cognitive load) and others are affective (e.g., confusion, boredom, 

frustration, anxiety, anger). It is essential for the ITS to be able to recognize and 

react appropriately to these states to either optimize positive influence or mitigate 

negative influence. 

In investigating significant influencers of learning, we found the following in the 

literature or through our own investigations: 

 Cognitive modeling: cognitive load, engagement (Lepper and Woolverton

2002); attention, distraction, drowsiness, engagement, flow, and workload 

(Carroll et al. 2011; Kokini et al. 2012)

 Motivational modeling: personality, values, goals, interests (Lepper and

Woolverton 2002)

 Affective modeling: confusion, boredom, frustration, engagement/flow, 

curiosity, anxiety, delight, and surprise (Graesser and D’Mello 2012); 

mood modeling—pleasure, arousal, and dominance (Mehrabian 1996; 

Sottilare and Proctor 2012; Brawner 2013)

A study by Brawner (2013) examined methods to classify cognitive and affective 

states. One goal is to be able to build classifiers that can be applied across 

populations. Brawner found that generalized classifiers were proving to be 

impractical because of individual differences. Offline individual classification 

models turned out not to be reusable on the same individuals due to changes in 

physiology from one day to the next. Real-time classifiers of affect were of good 

quality (~80% accurate), but real-time classifiers of cognition were not as good 

(<60% accurate).

Brawner’s study used a variety of classification methods. If you are interested in 

other machine learning techniques, check out WEKA, an open-source software 

tool for machine learning: http://www.cs.waikato.ac.nz/ml/weka/.

5.2 Instructional Management in Support of Self-Regulated 

Learning

In this section we will discuss the relationship between learner state and the 

development of instructional techniques (policies) and instructional strategies 

(plans). We previously discussed CDT (Merrill et al. 1992) as a method of 

managing instruction based on learning theory. The construct of CDT forms the 

basis for policies implemented in GIFT. Policies are rules or constraints that the 

ITS agents monitor to manage the flow, pace, and challenge level of the 

instruction based on the states and progress of the learner. Strategies are the 
14

recommended plans or course of action that the ITS develops based on the 

learners states. Tactics, which are implemented by the domain module, are actions 

formulated by the ITS based on the strategy recommendations and the specific 

domain context (who, what, where, when, and how).

CDT is just one approach to instructional management in support of SRL. Just 

about any learning theory could be instantiated in a tutoring framework to support 

SRL, but policies must be deconflicted to prevent paradoxes and ambiguities,

which might result in negative training. This is why intelligent agents are critical 

to monitoring policies and decisions by the tutor. 

Another approach to instructional management in support of SRL is modeling the 

behaviors, processes, and successes of expert human tutors as follows:

 INSPIRE model (Lepper et al. 1997). (INSPIRE = intelligent, nurturant, 

Socratic, progressive, indirect, reflective, and encouraging.)

 Facts about human tutoring (Person and Graesser 2003)

 Importance of questioning (Dillon 1988) 

 Relation between deep reasoning questions and exam scores (Graesser and

Person 1994)

 Politeness strategies (Person et al. 1995)

Another approach is to mimic ITS processes, which have been applied and have 

been shown successful over time. Such a process developed by Person et al.

(1995, p. 167) has been applied extensively in dialogue-based tutoring, as shown 

in Fig. 11.

Fig. 11 Application of Person’s (1995) 5-step tutoring process
15

Another approach is to investigate the learner’s perception of the tutor to identify 

specific characteristics that influence learning. Anything that enhances the 

relationship between the learner and the tutor will result in fewer interventions by 

the tutor (resulting in more efficient learning processes) and higher levels of 

engagement (resulting in more opportunity to learn). Studies identified in the 

literature related to learner perception of the tutor are as follows:

 Credibility and supportiveness of the tutor (Holden 2012)

 Learner expectations of the tutor (Holden and Goldberg 2011)

 Social pedagogical agents (Kim et al. 2008)

 Characteristics of learning companions (Kim 2007; Kim et al. 2007)

A future approach to instructional management for SRL during tutoring is to 

evaluate the relationship between successful instructional strategies and the 

domains in which they are applied. One way to segregate this domain analysis is 

along the lines of Bloom’s taxonomy (1956; cognitive, affective, psychomotor,

and social) and examine which strategies are most successful in each domain. 

Later these might be applied as policies for tutoring in that domain. In order to 

instruct effectively in a domain, we must understand how to measure success of 

the learner in order to develop appropriate strategies. Bloom’s taxonomy 

identifies hierarchies that might be used in the future to measure learner success 

in a broad domain. For example, affective learning is related to values, and one of 

the high-level behaviors of learners that indicate emotional growth is organizing 

values into priorities by comparing, relating, and synthesizing values to support 

decision making. The ability of the tutor to recognize this state will allow more 

effective decision making and enhanced learning.

5.3 Domain Modeling in Support of Self-Regulated Learning

In this section we will discuss the process of ITS decision making leading to tactic 

selection and delivery. Tactics are actions by the tutor that vary by domain. 

Tactics employed during cognitive tasks differ from those employed during 

psychomotor tasks. In order to grow ITSs beyond desktop training domains to 

more complex and dynamic military tasks, we need to examine how tactics will 

change and how the mode of delivery will also be affected. As shown in Fig. 12, 

we have identified 3 dichotomies for expanding the application of ITSs into 

military domains.

The simple-complex dichotomy is probably the easiest to tackle in the near term. 

Complex tasks can be segregated into a number of simpler tasks for presentation 
16

to the learner. The well-defined–ill-defined dichotomy is more difficult to 

implement. Given the nature of ill-defined tasks, intermediate measures of success 

are more difficult to define, so there is heavy emphasis on outcomes (e.g., 

performance), making it difficult to author instruction. An example of an illdefined domain is leadership in a military context and hitting a baseball or golfing 

in a civilian context. 

Fig. 12 Dimensions of domain modeling for military training

Finally, in the third dichotomy, static-dynamic, the concept is to more closely 

match the mode of interaction with the nature of the task. In other words, it is 

reasonable and effective to train a cognitive task (e.g., decision making) in a 

desktop training environment (e.g., game-based tutor), but it may not be as 

effective to train a psychomotor task (e.g., marksmanship or repelling) in a 

desktop or static mode because there is not an opportunity for learners to train as 

they would in the operational environment. For this reason, we developed a 

hierarchy of interaction for various tasks, as shown in Figs. 13–16.

Static environments (desktop mode) support purely cognitive and affective tasks

where a low degree of interaction with the environment and other learners is 

critical to learning, retention, and transfer to the operational environment. 

Decision-making and problem-solving tasks are taught easily in a static mode, as 

shown in Fig. 13.
17

Fig. 13 Static or desktop interaction with adaptive tutors

Limited kinetic environments support hybrid (cognitive, affective, psychomotor) 

tasks where a larger degree of interaction with the environment and other learners 

is critical to learning, retention, and transfer to the operational environment. 

Decision-making and problem-solving tasks may be taught easily in a limited 

kinetic mode along with tasks requiring physical orientation (e.g., land 

navigation), as shown in Fig. 14.

Fig. 14 Limited kinetic interaction with adaptive tutors

Enhanced kinetic environments support tasks where freedom of movement and a 

high degree of interaction with other learners are critical to learning, retention, and 

transfer to the operational environment. Building clearing and other team-based 

tasks may be taught easily in an enhanced kinetic mode, as shown in Fig. 15.

Fig. 15 Enhanced kinetic interaction with adaptive tutors
18

In the wild mode is transferring tutoring to the operational environments and 

could also be called embedded training for Soldiers. In the wild mode is critical to 

support tasks where a very high degree freedom of movement and a high degree 

of interaction with other learners are critical to learning, retention, and transfer to 

the operational environment. Psychomotor and social tasks may be best taught in 

the wild, as shown in Fig. 16.

Fig. 16 Adaptive tutoring in the wild

The challenge with each of these modes is the increasing difficulty of 

unobtrusively acquiring their data, classifying their states, and providing relevant, 

real-time feedback to one or more members of a team.

5.4 Authoring in Support of Self-Regulated Learning

In this section we will discuss the process of ITS authoring or development. To 

support SRL, authoring processes must be able to support extensively adaptive 

training scenarios that tailor learning to a high degree. To support higher levels of 

adaptiveness (read this as larger numbers of options), authoring becomes more 

complex and tedious unless the authoring process can be streamlined. Two

principles can be applied to the authoring process to reduce the author’s workload 

but still allow flexibility for the author to control the instructional process in detail 

if needed.

Principle #1: Avoid Authoring by Promoting ITS Standardization, 

Interoperability and Reuse

Promote modularity to a large degree within the authoring process. Standards for 

processes, interaction, and exchange of data between modules (read this as a 

framework) will reduce the need for authoring. Standardization will also allow for 

reuse on a large scale. Templates and graphical interfaces will reduce workload 

and allow authors to organize knowledge and content.

Principle #2: Avoid Authoring through Automation

Wherever you are unable to avoid authoring new content, employ automation. 

Evaluate processes to determine the most tedious as candidates for automation. 

Processes that must be repeated frequently are candidates for automation. Within 

GIFT we automated a large portion of the expert modeling process, which is used 
19

to model the ideal learner for a particular domain. Artificial intelligence 

techniques in the way of job aids can be used to guide new authors through the 

authoring process.

This section and the tutorial ended with a demonstration of the GIFT authoring tools.
20

INTENTIONALLY LEFT BLANK.
21

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