Intelligent Learning Environments

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world chess champion Garry Kasparov. Nineteen years later .... Cognitive theories such as John Anderson's ACT-R (Adaptive Control of Thought—. Rational) ..... I would like to dig deeper into this process to understand how AI designers meet ...

Travail Noté #1 Environnements d’Apprentissage Intelligents (Intelligent Learning Environments)

Par Stephen Hanley TED 6520 – Environnements d’Apprentissage Intelligents Valery Psyché, chargé d’encadrement 7 de juillet de 2017

Table of Contents

Page #

1. Introduction


2. History of Intelligent Learning Environments


3. Characteristics of Intelligent Learning Environments


4. Types of Intelligent Learning Environments


5. Research Questions


6. Conclusion


7. Bibliography


Environnements d’Apprentissage Intelligents - EAI Intelligent Learning Environments – ILE

1. Introduction The world was caught by surprise in 1997 when Deep Blue, IBM’s supercomputer, demonstrated impressive progress in the development of artificial intelligence (AI) by defeating world chess champion Garry Kasparov. Nineteen years later, on March 12, 2016, a news headline: “Google’s AlphaGo Defeats Go World Champion for the Third Time” added new evidence of how far AI had advanced in less than two decades. Highlighting the impact of AI on every aspect of life today, website lists more than sixty news headlines from 2016 alone that focus on AI. Not surprisingly, companies like Microsoft, Apple, IBM, and Google are investing millions of dollars in AI research. On the other hand, many prominent experts, scientists, and business leaders including Stephen Hawking, Bill Gates, and Elon Musk express fears over where future developments in AI may lead. Despite these worries, there is a consensus among educators, psychologists, and other specialists that the field of education can only benefit from new applications of AI. One of these applications, intelligent learning environments (ILEs), is the focus of this paper, which presents a synthesis of the positions, arguments, and conclusions of authors who have studied the domain for many years and especially those whose works (Le Domaine des Environnements d’Apprentissage Intelligents and Environnements Informatiques pour l’Apprentissage Humain) provide a foundation for this analysis.

2. History of Intelligent Learning Environments At this point, we should define some terms that are intrinsic to the domain. The term learning environment, in general usage, refers to the settings or conditions in which students learn, including social, emotional, and cultural aspects as well as physical locations. While intelligent learning environments (ILE) share these same elements, they have unique characteristics that are made possible by artificial intelligence (AI). During the 60s and 70s, a series of AI-related advances prepared the way for the development of ILEs including computerassisted instruction (CAI), the implementation of course management features and user roles (CMS), Arpanet (later the Internet), natural language processing (NLP), and intelligent tutoring systems (ITSs) (Anon., 2016). Improved hardware capabilities and theoretical contributions from 1

researchers in the fields of pedagogy, education, cognitive science, and information and communication technology also played their part. Early contributions in the field of programmed learning were made by B.F. Skinner (1953) and British educational theorist and cybernetician Gordon Pask (Skinner, 1958) (Waters, 2015). Skinner and Sidney Pressey developed teaching machines based on Skinner’s theory of operant conditioning1, and Pask invented the self-adaptive keyboard instructor (SAKI). The following year, research psychologist Frank Rosenblatt began his work on theoretical nerve nets and created the first algorithmically described neural network (Rosenblatt, 1962). The resulting algorithm, which he named perceptron, provided the foundation for machine learning2 (Kurenkov, 2015), an important step towards computer-assisted instruction (CAI), intelligent tutoring systems (ITS), and intelligent learning environments (ILE). During the 60s and early 70s, numerous AI developments in information processing and the cognitive sciences were applied to the field of education, contributing to the creation of the first ILEs (Bourdeau, 2014). An early application of artificial intelligence in education focused on computer-assisted instruction (CAI) and computer-managed instruction (CMI). In 1960, an experimental system called PLATO3, the first generalized computer-assisted instruction system, was designed and built at the University of Illinois. Designed to study the feasibility of computer-based education (CBE), the PLATO system featured four user roles—student, instructor, author, and multiple, and incorporated characteristics that would eventually become standard components of intelligent learning environments. David R. Woolley, in his 1994 article PLATO: The Emergence of Online Community, provides this interesting insight: “Two decades before the World Wide Web came on the scene, the PLATO system pioneered online forums and message boards, email, chat rooms, instant messaging, remote screen sharing, and multiplayer games, leading to the emergence of what was perhaps the world's first online community” (Woolley, 1994). PLATO and other CAI systems provided individual, interactive, computer-based instruction and introduced many modern computing concepts that are essential elements of


Operant conditioning is a term coined by B. F. Skinner based on behaviorist learning theory that explains behavior on the basis of the causes of an action and its consequences. Skinner proposed that behaviors that are rewarded (reinforced) tend to be repeated, while behaviors that are punished, tend to be weakened and extinguished. 2 Machine learning is a type of artificial intelligence (AI) which "gives computers the ability to learn without being explicitly programmed". 3 PLATO (Programmed Logic for Automatic Teaching Operations)


today’s ILEs. They were particularly suited to delivering instructional materials, drill-andpractice type exercises, simulations, learning games, and tutoring. However, they could not be classified as “intelligent” systems for several reasons—instructional materials were primarily composed of static content pages structured as a series of questions, answers, and feedback; learning was programmed in a fixed step-by-step sequence; feedback was standard and not adaptive; and the user interface was sparse and short on communication tools (Bourdeau, 2014). Nevertheless, during the early 1970s, educational researchers began to adopt concepts from artificial intelligence to create rich learning environments, and a new generation of CAI evolved—ICAI, intelligent computer-assisted instruction, and ITSs, intelligent tutoring systems (Clancey & Soloway, 1990). For example, James Carbonell, in a 1970 journal article, presented evidence that intelligent computer-assisted instruction was possible by integrating CAI with an “extensive application of artificial-intelligence (AI) techniques” (Carbonnell, 1970). In 1982, Derek Sleeman and John Seely Brown published an up-to-date review of ICAI research in the book Intelligent Tutoring Systems, a collection of papers detailing work in this field. It included thirteen papers, seven of which come from a special 1979 issue of the International Journal of Man-Machine Studies (January 1979) (Auguste, 1985). Benjamin Bloom’s 1984 article showing that individual tutoring was twice as effective as group teaching fueled the AI researchers’ dream of developing intelligent systems that could provide students with effective tutoring tailored to their individual needs and pace of learning. Cognitive theories such as John Anderson’s ACT-R (Adaptive Control of Thought— Rational) theory proposed cognitive architectures with individual processing modules to explain how the brain was organized and produced cognition. Such cognitive architectures could be implemented on a computer to create models of human cognition that produced step-by-step simulations of human behavior and could be used to support and evaluate learning. Anderson argued that ITSs should be capable of reasoning on and working out the solutions to the problems they presented to students (Bourdeau, 2014). In 1987, Etienne Wenger wrote a seminal work entitled Artificial Intelligence and Tutoring Systems in which he “established what would become the so-called ‘traditional ITS architecture’ with its four components: domain, student, tutor, and user interface” (Nkambou, et al., 2010) . In 1990, the future president of the AIED society, John Self, stressed the need for establishing theoretical foundations for ITSs so that they could be perceived as an engineering 3

design field (Self, 1990) (Nkambou, et al., 2010). Since then, student/learner modeling has been at the heart of much of Self’s research and has significantly influenced the AIED field (Kay & McCalla, 2003). A generation later, Beverly Woolf published a comprehensive survey of the ITS field in her 2008 book Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning, and in LNCS (Lecture Notes in Computer Science) volume 5091 from the 2008 ITS conference, co-authored an up-to-date description of the latest developments in ITS (Woolf, 2008) (Woolf, et al., 2008). Other milestones on the path to ILEs were the creation of the above-mentioned Artificial Intelligence Society (AEID) in 1997, its IJAIED journal, and its biannual symposiums that have alternated with a series of Intelligent Tutoring Systems symposiums that began in 1988 (Bourdeau, 2014). Intelligent computer-assisted instruction (ICAI) supported by intelligent tutoring systems (ITS) gradually evolved into intelligent learning environments (ILEs) as learning systems or platforms combined intelligent, computer-assisted instruction with the software and communication tools needed to create a student-centered4 environment that provided personalized instructional materials and procedures and a user interface that facilitated studentdriven learning and knowledge acquisition (Carbonnell, 1970). Emerging in the 1980s, authoring systems—software environments specialized in the development of ITSs—contributed significantly to the creation and wider use of ILEs. (Bourdeau, 2014)

3. Characteristics of Intelligent Learning Environments Jacqueline Bourdeau, in her 2014 text Le Domaine des environnements d’apprentissage intelligents, identifies the two pedagogical strategies that are used to implement intelligent learning environments (ILE)—guidance and discovery. ILEs that opt for guidance learning adopt intelligent tutoring systems (ITS) as their primary element, whereas those that focus on discovery learning are developed in accordance with inquiry-based learning theory (Bourdeau, 2014). Although efforts have been made to develop ILEs that integrate both strategies, historically, discovery learning has not played an important role in ITSs due to the high degree of learner independence that this strategy entails (Veermans & Van Joolingen, 1998).


Student-centered in this context refers to a learning environment that is developed around a student model or an open learner model, both of which, within ITS or ILE, represent the student’s knowledge, traits, and physical environment.


The characteristics of an ITS-based ILE basically mimic those of the ITS it contains. However, they do differ in that an ILE covers a broader spectrum than an ITS. Since the term “environment” refers to the totality of the surroundings or conditions in which a person operates, an ILE includes elements that are not found in an ITS alone. These additional elements may include computer-supported collaborative learning, content management, and administrative elements among others. In this type of ILE, these components complement the ITS and contribute to a more complete learning environment that adapts the learning process to learners and provides them with a greater assortment of tools. Back in 1994, Brusilovsky defined the concept this way: “An intelligent learning environment is a relatively new kind of intelligent educational system which combines the features of traditional Intelligent Tutoring Systems (ITS) and learning environments”. Regarding the interaction between the student and the ILE, he added: “an intelligent learning environment (ILE) includes a special component to support student-driven learning, the environment module,” and “a special component [...] which provides access to structured instructional material” (Brusilovsky, 1994). The nature of this type of ILE is better understood after taking a closer look at the architecture of its main component—the ITS. As mentioned earlier, traditional ITS architecture is comprised of four components: the domain model, the student model, the tutor model, and the user interface model. The domain or expert model contains the expert knowledge—concepts, rules, and problem-solving strategies—of the field that is being studied and can solve domain problems. The objective of the domain model is to capture knowledge from experts and explicitly represent it in a way that can be used by computer-based systems. Wenger explains: “It is then the responsibility of programs to compose instructional interactions dynamically, making decisions by reference to the knowledge with which they have been provided” (Wenger, 1987). When the knowledge elements are linked in pedagogical sequences in a “dynamic model, according to various structures such as hierarchies, semantic networks, ontology and production rules”, knowledge and pedagogy come together in a curriculum structure (Nkambou, et al., 2010) (Bourdeau, 2014). In addition to being the source of expert knowledge, the domain model may fulfill other roles such as providing a benchmark for evaluating learner performance and detecting errors. Given the instructive nature of the domain model, domain designers face two important challenges—adequate knowledge acquisition and knowledge representation.


Notwithstanding the importance of the domain model, the core component of an ILE is the student or learner model. Ideally, the system’s student model should be well-documented on the student’s pre-existing knowledge of the subject domain, his or her skills, motivation, attitudes, affect, goals, culture, etc. To be adaptive, it must be dynamic, evolving with the student as the learning process advances. Étienne Wenger and John Self were among the first to define the major roles of the student model. According to Wenger (1987), the student model carries out three main tasks: 1) it obtains explicit and implicit data about and from the student; 2) it uses this data to build a representation of the learner’s knowledge and learning process; and 3) it performs a diagnostic analysis of the data to determine the student’s status regarding his or her knowledge acquisition and learning, and selects the best pedagogical strategies for presenting additional domain information. Similarly, Self (1988), describing the roles of the student model, set out six major functions: 1) a corrective function to rectify errors in student knowledge; 2) an elaborative function to fill in gaps in student knowledge; 3) a strategic function to introduce meaningful changes in the tutorial strategy in areas not covered by roles one and two above; 4) a diagnostic function to identify errors in student knowledge; 5) a predictive function to anticipate probable student answers or reactions to tutorial actions; and 6) an evaluative function to provide assessments of students and of the ITS itself (Nkambou, et al., 2010) (Wenger, 1987). Since then, the student model has continued to evolve mainly due to social changes and advances in technology evidenced in the way people are using communication technologies—social media to communicate with others and the web to obtain information (Nkambou, et al., 2010). The third component in an ITS is the tutor model. The term “tutor”, associated with “instruction” and “education”, is defined by its two principle properties: 1) individualized instruction (in most cases the tutor/student ratio is between 1:1 and 1:3); and 2) a situation in which tutor control and guidance exist (Collins, 2006). In this context, instruction focuses on the instructor’s role, learning on the learner’s role, and tutoring on the personalized interactions that occur during the instructional-learning process. In such interactions, control may be shared— first, when the tutor leads, questioning the learner, and then, reacting to the student’s answers, adapts to the learner. In ILE design, the main feature of the tutor model is the interaction it enables between the “intelligent tutor” and the learner. Ideally, intelligent tutors should be able to match the ability of good human tutors—knowing what to teach and when and how to teach it. VanLehn highlights an important factor when he notes that tutoring achieves consistently 6

increasing effectiveness in direct proportion to the amount of interaction provided by the system (VanLehn, 2008). From a design perspective, the tutor model may be independent, or in other cases, integrated with either the student model or the domain model (Bourdeau, 2014). The greatest challenge in designing the tutoring component of an ITS, is to develop an intelligent software (AI) that can reason on the data being received from learner-system interactions, adapt to the learner’s needs in real time, and reshape its tutoring accordingly (Nkambou, et al., 2010). The fourth component of an ITS is the user-interface model—the component that enables communication between the learner and the system and provides the tools needed to carry out the interaction process. Raskin defines the system interface as “the way you accomplish tasks with a product, what you do and the way it reacts” (Raskin, 2000). Given the number of pedagogical strategies available to ILE designers, the task of designing the user interface is complex, one that must be developed simultaneously and in coordination with the instructional design, educational architecture, and interoperability elements. Among the many factors affecting interface design are those focused on solving cognitive, organizational, and social problems in addition to ILEspecific issues involving pedagogy and didactics. As a result, no single set of characteristics suffices to delineate the interface model in ILEs (Delozanne, 2006). The second pedagogical strategy used to implement intelligent learning environments (ILE) is discovery learning. Discovery learning is based on constructivist learning theory, which states that learners construct new knowledge as they make connections between new information and the knowledge they already possess. Discovery learning usually takes place within one of the following environments—one based on tutorial dialogue and the other on the exploration of simulated reality as in simulations or microworlds (Bourdeau, 2014). Computer simulations use mathematical models to digitally recreate the representation of a process and bring phenomena from the real world into the learning environment, while micro-worlds, a computer program that enables students to develop interactive games, carry out mathematical experiments, and examine scientific simulations, encourages learners to develop abstract ideas through experimentation. ITS and discovery-based ILEs share the same basic architecture; their difference lies primarily in the content of the models. Whereas the domain model in an ITS models the knowledge of an expert and identifies a student’s difficulties by comparing his or her solution to that of the expert, the domain model in a discovery-based ILE models knowledge of the content to be learned in a way that permits the learner to discover it. Instead of having their mistakes 7

pointed out explicitly, learners in discovery-based ILEs are helped to discover the errors in their reasoning that may have led to mistaken results and to make the adjustments needed to reach the correct understanding. The incorporation of learning situations that resemble real life adds practical value to the process and often contributes to improved student motivation. The student model in discovery learning ILEs also differs significantly from that found in ITS learning environments due to differing pedagogical strategies: ITSs are guidance-oriented and try to adjust the learner’s path to that of the domain expert, while discovery learning adopts a constructivist perspective and focuses on inquiry-based learning. From the constructivist perspective, all efforts adopted by the student to solve a problem are acceptable, and even when they contain errors, these represent additional opportunities for learning (Bourdeau, 2014). Discovery learning environments offers developers two design paths: tutorial dialogue or simulation. Tutorial dialogue finds support in the concept of scaffolding based on Vygotsky’s social-constructivist theory of Zone of Proximal Development (Anon., 2010). However, in this scenario, two problems arise—when and how to intervene in the learner’s activities. Addressing this issue, the pedagogical model incorporates a body of rules that determines when to trigger the tutorial dialogue and what approach to take. On the other hand, when the designer opts for a simulation and exploration environment, the most important prerequisite is that learners be provided with the tools they need to observe and explore within the simulation or microworld, to collect data, to formulate and support hypotheses based on the collected data, and finally, to evaluate their hypotheses (Bourdeau, 2014). In both cases, student support is important, and Vygotsky’s scaffolding analogy favors the use of a coach to engage in dialogue with the learner and provide instructional support (Anderson, 2011). The user-interface model may also vary depending on whether the ILE adopts tutorial dialogue or simulation as its pedagogical strategy.

4. Types of Intelligent Learning Environments In the previous section, we identified the two main types of intelligent learning environments as Intelligent Tutoring Systems (ITS) and Discovery Learning Environments. A major step in their development was the birth of computer-assisted instruction (CAI), and although CAI did create learning environments, they could not truly be classified as “intelligent” learning environments because they could not provide advanced personalization or an environment tailored to learners’ individual learning goals and needs (Bourdeau, 2014). 8

Whatever the development strategy chosen by designers, all ILEs are created to facilitate learning and therefore incorporate some form of cognitive modeling, be it proposing problems that need to be solved or using a series of exercises to achieve specific learning goals. Information processing is also inherent to the two models since they both use computer systems and AI software to create learning environments. Authoring systems, a third type of ILE, also create knowledge-based environments but are a design tool rather than a teaching tool. They are used to simplify and expedite the production of ILEs by performing parts of the design process automatically or semi-automatically (Bourdeau, 2014). As is true of the first two types, authoring systems vary in design and generally correspond to the type of ILE they are designed to produce. How does cognitive modeling affect the design of individual ILEs? When designing an ILE, developers choose a cognitive model on which to base their system, and the teaching and learning theories and pedagogical strategies they adopt generally establish the starting point for their development. For example, developers who favor the behaviorist and cognitivist paradigms will usually opt for an ITS-based design while those who privilege constructivist theory will most likely decide on a discovery learning ILE. Developers have many learning theories to choose from; however, behaviorism, cognitivism, and constructivism have dominated the field since the beginning of the twentieth century (de Vries & Baillé, 2006). Based primarily on the work of J. B. Watson, E. L. Thorndike, and B. F. Skinner, behaviorism became the dominant learning theory, especially in North America, during the first half of the 20th century. To this day, key assumptions that underlie instructional design are based on behaviorist theory, and learning environments featuring CAI and even ITS typologies often implement the behaviorist model for learning objectives that involve well-defined, highlysequenced, and structured curricula or skills that can be learned through reinforcement and practice (Hammond, et al., 2001). In ILEs that include a behaviorist approach, the domain model must clearly identify what learners will have learned once the instructional process has finished, evaluate their learning results, and strengthen appropriate stimulus→response associations through repetition and practice. Starting in the late 1950s, a growing interest in the cognitive processes of learning—the way people perceive, think, remember, and learn—led to the development of cognitive learning theories that have replaced behaviorism as the dominant learning paradigm. Cognitivists theorized that the memory system actively processes and stores information, and that prior 9

(stored) knowledge plays an important role in human learning. Based on computer analogies, information processing theorists employed computer terms such as memory, storage, encoding, and retrieval in developing information processing theory, which contributed significantly to the evolution of CAI into ITS-based intelligent learning environments (de Vries & Baillé, 2006). Rooted in cognitive psychology, constructivism has played a significant role in educational literature since the 1950s, having a great impact on both teaching and learning. Constructivism attempts to explain how knowledge is created within the person, individually or in a social context, rather than being acquired from external sources. An accompanying theory, pragmatism, focuses on the practical value of thought and the need to act on ideas to test their validity in real life experiences. These two theories lend themselves more to a discovery-learning ILE than an ITS. To assign learners a more active role in their learning process, designers may create a “tutor” model that accompanies learners through coaching rather than instructing them by teaching. Depending on the ILE design, in discovery learning environments, learners may explore individually, in the company of a virtual learning companion, or in collaboration with an online community of learners. Examples of discovery include the virtual exploration of natural laws, the hands-on development of skills, or even the discovery of knowledge that is not physically observable such as self-knowledge or metacognition. When designers opt for an openlearner model and provide the necessary tools, learners can see their personal student model in the system and even contribute directly to its design and modification (Bourdeau, 2014). A second area that significantly affects ILE design is information technology and system architecture. Because information technology enables designers to choose from a broad spectrum of services, the services they prioritize define system architecture and the AI techniques used to implement that architecture whether it be classic, multi-agent, or adaptive-hypermedia. Classic ILE architecture is characterized by the four models (domain, student, tutor, user interface) discussed in the previous section and employs interfaces based on AI techniques that implement knowledge representation and reasoning. Multi-agent ILE systems are computerized systems that incorporate multiple, autonomous, interactive software agents within the learning environment. Individual agents enjoy their own knowledge representation and reasoning systems and can be used in many ways, functioning, for example, as one of the four models, as a service provider, or as a learner support system. Not only does the modularity of multi-agent systems enable them to handle complex tasks, but it also favors the sharing of knowledge within the system, the 10

reutilization of system components, and greater flexibility for future modifications and growth. Adaptive-hypermedia architecture provides designers with other options to facilitate learning. With adaptive hypermedia, links to knowledge are tailored to individual student models that contain the learner’s personal, cognitive, and knowledge profiles, enabling the ILE to adapt to aspects such as the student’s goals, knowledge, background, and preferences. Distribution requirements such as providing for collaborative learning, distance learning, or eLearning are another area where system architecture must adapt (Bourdeau, 2014). Other factors influencing ILE design include domain knowledge, learning objectives, knowledgerepresentation formalisms, reasoning systems, and student support considerations. Also, since human learning in an ILE requires that humans and machines interact, ILE design must consider hypotheses that explain what occurs in human-machine interactions (de Vries & Baillé, 2006). Knowledge representation and reasoning are the two AI elements in an ILE that make human-machine interactions possible. ILEs are knowledge-based computer systems composed of three sub-systems: a knowledge base, an inference engine, and a user interface. The knowledge base contains structured and unstructured information that represents facts about the world. The inference engine is the reasoning sub-system that applies logical rules to the knowledge base and deduces new knowledge, while the third sub-system, the user interface is the space where humans and machines interact. For knowledge of the world to be processed by a computer, it must be represented, i.e. it must be expressed in a manner that has meaning to the machine. However, in an ILE, communication is bidirectional, so information (knowledge) about a domain, tutor, or student must be represented in a way that can be understood by human users and processed by a computer system. Many factors affect the way knowledge representation is implemented. Two factors are the domain to which it belongs and the nature of the reasoning it requires. Three principal knowledge representation formalisms are: logic, semantic networks, and frames. Logical representations express factual or declarative knowledge and the basic rules related to the domain on which reasoning will occur. Semantic networks store information as concepts that are related to each other in a machine-understandable way. They represent knowledge in graphs from which more complex relationships of information can be constructed and conceptual meaning obtained. Frames permit the coding of knowledge that requires inferences and is especially convenient in representing objects that are typical to stereotypical situations and 11

commonsense knowledge. Slots and fillers are used to define objects, and these along with inheritance allow a frame system to provide powerful knowledge representation (Bourdeau, 2014). The inference engine or reasoning sub-system in an ILE represents knowledge by drawing information from the knowledge base and applying logic rules to generate conclusions and deduce new knowledge. Production rules, for example, enable an ILE to behave intelligently. The six main classes of reasoning systems are: deductive, inductive, abductive, analogical, metareasoning, and case-based (Russell & Norvig, 2010). Deductive reasoning can be applied in most types of knowledge representations. Abductive reasoning provides “most likely” hypotheses resulting from logical inferences based on observations; therefore, the premises do not guarantee the conclusion. Inductive reasoning, although inherently uncertain, can provide strong conclusions in direct proportion to the strength of the premises they are founded on. Case-based reasoning proposes that new problems can be solved based on the way similar problems were solved in the past. This form of reasoning is especially appropriate for structured representations such as frames (Bourdeau, 2014). The six types of reasoning mentioned above are not all equally effective with all kinds of knowledge representation, so developers must choose the most appropriate type to apply when designing an ILE. The complexities of designing and developing ILEs might lead some to conclude that despite the promise they hold for improving education, their widespread use is doubtful. Aleven, et al. believe that authoring systems are the solution (Aleven, et al., 2009), and that significant growth in the use of ILEs may depend on the ability of authoring systems to: 1) reduce the necessity of creating new ILEs from scratch; 2) provide a standard basis for comparing ILEs; and 3) bring down the cost of producing ILEs. Authoring systems can be divided into four large groups: 1) systems focused on teaching; 2) systems focused on performance; 3) systems focused on interactions in an IST-based ILE; and 4) systems based on discovery learning. The diversity of authoring systems parallels that of the other two types of ILE as they too center on the same paradigms. This limits the possibility of sharing and reusing system components, a problem that was addressed by Murray in his 1999 book Authoring Intelligent Tutoring Systems: An Analysis of the State of the Art (Murray, 1999).


5. Research Questions After months of studying ILEs, I can confirm the adage that “the more you know, the more you know you don’t know” (Aristotle). I share the final reflections of the many authors whose books and articles I had the opportunity to peruse. I was impressed by the fact that in spite of their expertise in this field of investigation, they all raised questions that require additional research. The three areas that interest me the most for further research follow. First, I would like to research ITSs that could be used to support the teaching of English as a second language. I have been teaching English as a second language for more than thirty years, the past fifteen years with a well-known language school in Montreal. However, I doubt the efficiency and effectiveness of intensive courses for students who are being force-fed much more information than they can possibly absorb in the assigned time. On the other hand, hiring a personal tutor would be prohibitively expensive considering the number of hours needed to learn and master a new language. I am convinced the solution is an ITS designed for language teaching with a human coach available for when learners require special assistance. The second area I would choose for additional research is the tutor model of an ITS. I understand the principles that enable the tutor model to function within an ITS; however, I would find it fascinating to learn how an AI enables a virtual tutor to adapt to learners dynamically and adjust guidance accordingly. I would like to dig deeper into this process to understand how AI designers meet the challenge of developing software that can reason on the data obtained from learner-system interactions, adapt to the learner’s needs in real time, and reshape tutoring accordingly. I would also like to investigate the progress that has been made in enabling an ITS to measure a learner’s affect and keep learners motivated when they are using an ITS for distance learning. The third question I would like to investigate is related to the domain model. Because I would like to offer my students the possibility of studying English online, I need to begin to think about creating the expert system that would be the heart of the domain model. My first step would be to look for existing, successful domain models that can illustrate best practices. Since an authoring tool would be a valuable tool, I will also have to research and evaluate them on the basis of their ability to offer adequate representation of content and pedagogy, features and tools to facilitate authoring, and a sufficient degree of flexibility. 13

6. Conclusion In this paper, we have seen that the foundations for creating intelligent learning environments (ILEs) were laid in the 1960s and 1970s with the development of computerassisted instruction (CAI), the implementation of course management features and user roles (CMS), the Arpanet (later the Internet), natural language processing (NLP), and intelligent tutoring systems (ITSs) preparing the way. Technological advances in information processing and especially in computer processing speed and memory capacity played an important role as did the theoretical contributions of researchers in pedagogy, education, cognitive science, and information and communication technology. We described the two pedagogical strategies used to implement intelligent learning environments—guidance and discovery, and the defining characteristics of three types of ILE: ITS-based, Discovery-based, and Authoring Systems. Key concepts were presented in detail by identifying the four models in ITS-based ILEs, explaining how cognitive modeling, information processing, and system architecture affect ILE design, and defining knowledge representation and reasoning, the two AI elements in an ILE that make human-machine interactions possible. Since the early 1970s, ILEs have made enormous progress, and although the end is nowhere in sight, investigation and experimentation continue to advance with the enthusiastic efforts of dedicated and able researchers. As we go forward, improved authoring systems should significantly reduce the time and cost of developing new ILEs, and we expect that with time the use of ILEs will become widespread and make an important contribution to improving education.


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