Intelligent tutoring systems research in the training systems division: Space applications

Computer-Aided Instruction (CAI) is a mature technology used to teach students in a wide variety of domains. The introduction of Artificial Intelligence (AI) technology of the field of CAI has prompted research and development efforts in an area known as Intelligent Computer-Aided Instruction (ICAI). In some cases, ICAI has been touted as a revolutionary alternative to traditional CAI. With the advent of powerful, inexpensive school computers, ICAI is emerging as a potential rival to CAI. In contrast to this, one may conceive of Computer-Based Training (CBT) systems as lying along a continuum which runs from CAI to ICAI. Although the key difference between the two is intelligence, there is not commonly accepted definition of what constitutes an intelligent instructional system

8. Issues in Intelligent Computer-Assisted Instruction: Eval uation and Measurement

In this chapter we plan to explore two issues in the field of intelligent computer assisted instruction (ICAI) that we feel offer opportunities to advance the state of the art. These issues are evaluation of ICAI systems and the use of the underlying technology in ICAI systems to develop tests. For each issue we will provide a theoretical context, discuss key constructs, provide a brief window to the appropriate literature, suggest methodological solutions and conclude with a concrete example of the feasibility of the solution from our own research. INTELLIGENT COMPUTER-ASSISTED INSTRUCTION (ICAI) ICAI is the application of artificial intelligence to computer-assisted instruction. Artificial intelligence, a branch of computer science, is making computers smart in order to (a) make them more useful and (b) understand intelligence (Winston, 1977). Topic areas in artificial intelligence have included natural language processing (Schank, 1980), vision (Winston, 1975), knowledge representation (Woods, 1983), spoken language (Lea, 1980), planning (Hayes-Roth, 1980), and expert systems (Buchanan, 1981). The field of Artificial Intelligence (AI) has matured in both hardware and software. The most commonly used language in the field is LISP (List Processing). A major development in the hardware area is that personal LISP machines are now available at a relatively low cost (20-50K) with the power of prior mainframes. In the software area two advances stand out: (a) programming support environments such as LOOPS (Bobrow & Stefik, 1983) and (b) expert system tools. These latter tools are now running on powerful micros. The application of expert systems technology to a host of real-world problems has demonstrated the utility of artificial intelligence techniques in a very dramatic style. Expert system technology is the branch of artificial intelligence at this point most relevant to ICAI. Expert Systems Knowledge-based systems or expert systems are a collection of problem-solving computer programs containing both factual and experiential knowledge and data in a particular domain. When the knowledge embodied in the program is a result of a human expert elicitation, these systems are called expert systems. A typical expert system consists of a knowledge base, a reasoning mechanism popularly called an inference engine and a friendly user interface. The knowledge base consists of facts, concepts, and numerical data (declarative knowledge), procedures based on experience or rules of thumb (heuristics), and causal or conditional relationships (procedural knowledge). The inference engine searches or reasons with or about the knowledge base to arrive at intermediate conclusions or final results during the course of problem solving. It effectively decides when and what knowledge should be applied, applies the knowledge and determines when an acceptable solution has been found. The inference engine employs several problem-solving strategies in arriving at conclusions. Two of the popular schemes involve starting with a good description or desired solution and working backwards to the known facts or current situation (backward chaining), and starting with the current situation or known facts and working toward a goal or desired solution (forward chaining). The user interface may give the user choices (typically menu-driven) or allow the user to participate in the control of the process (mixed initiative). The interface allows the user: to describe a problem, input knowledge or data, browse through the knowledge base, pose question, review the reasoning process of the system, intervene as necessary, and control overall system operation. Successful expert systems have been developed in fields as diverse as mineral exploration (Duda & Gaschnig, 1981) and medical diagnosis (Clancy, 1981). ICAI Systems ICAI systems use approaches artificial intelligence and cognitive science to teach a range of subject matters. Representative types of subjects include: (a) collection of facts, for example, South American geography in SCHOLAR (Carbonell & Collins, 1973); (b) complete system models, for example, a ship propulsion system in STEAMER (Stevens & Steinberg, 1981) and a power supply in SOPHIE (Brown, Burton, & de Kleer, 1982); (c) completely described procedural rules, for example, strategy learning, WEST (Brown, Burton, & de Kleer, 1982), or arithmetic in BUGGY (Brown & Burton, 1978); (d) partly described procedural rules, for example, computer programming in PROUST (Johnson & Soloway, 1983); LISP Tutor (Anderson, Boyle, & Reiser, 1985); rules in ALGEBRA (McArthur, Stasz, & Hotta, 1987); diagnosis of infectious diseases in GUIDON (Clancey, 1979); and an imperfectly understood complex domain, causes of rainfall in WHY (Stevens, Collins, & Goldin, 1978). Excellent reviews by Barr and Feigenbaum (1982) and Wenger (1987) document many of these ICAI systems. Representative research in ICAI is described by O\u27Neil, Anderson, and Freeman (1986) and Wenger (1987). Although suggestive evidence has been provided by Anderson et al. (1985), few of these ICAI projects have been evaluated in any rigorous fashion. In a sense they have all been toy systems for research and demonstration. Yet, they have raised a good deal of excitement and enthusiasm about their likelihood of being effective instructional environments. With respect to cognitive science, progress has been made in the following areas: identification and analysis of misconceptions or bugs (Clement, Lockhead, & Soloway, 1980), the use of learning strategies (O\u27Neil & Spielberger, 1979; Weinstein & Mayer, 1986), expert versus novice distinction (Chi, Glaser, & Rees, 1982), the role of mental models in learning (Kieras & Bovair, 1983), and the role of self-explanations in problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1987). The key components of an ICAI system consist of a knowledge base: that is, (a) what the student is to learn; (b) a student model, either where the student is now with respect to subject matter or how student characteristics interact with subject matters, and (c) a tutor, that is, instructional techniques for teaching the declarative or procedural knowledge. These components are described in more detail by Fletcher (1985)

Applications of artificial intelligence within education

AbstractComputers have been employed within the field of education for many years, often with disappointing results. However, recent and current research within the field of artificial intelligence (AI) is having a positive impact on educational applications. For example, there now exist ICAI (intelligent computer-assisted instruction) systems to teach or tutor many different subjects; several such systems are discussed herein. In addition to CAI (computer-assisted instruction) systems, we discuss the development of learning environments that are designed to facilitate student-initiated learning. A third major application is the use of expert systems to assist with educational diagnosis and assessment. During the course of our discussion of these three major application areas, we indicate where AI has already played a major role in the development of such systems and where further research is required in order to overcome current limitations

Expert System Technology and Concept Instruction: Training Educators to Accurately Classify Learning Disabled Students

Many learning disabled student being served by the public school systems have been inaccurately classified. Training and research efforts are needed to assist members of the multidisciplinary team in making more accurate learning disabilities classification decisions. CLASS.LD2, a computer-based expert system, was designed to assist multidisciplinary teams by providing second-opinion advice regarding the appropriateness of a learning disabilities classification for individual student cases. The existing expert system, CLASS.LD2, was combined with strategies for effective concept instruction to create an instructional package entitled LO.Trainer. The purpose of this study was (a) to develop a computer-based instructional package combining expert system technology and strategies for effective concept instruction and (b) to test the effectiveness of the instructional package against another system application. The training application against which the instructional package was compared consisted of users running consultations with the original expert system. Of specific interest was (a) the effectiveness of both training programs across experienced and inexperienced teachers, (b) the performance of the experienced as compared with the inexperienced teachers regardless of the training program used, (c) whether an interaction between level of experience and training program occurred, ( d) which training program was more effective for the experienced teachers, and (e) which training program was more effective for the inexperienced teachers. Ninety-seven students from three universities served as subjects and were randomly assigned to one of the two treatment groups. Subjects who completed the LO.Trainer materials scored statistically (p \u3c .05) and educationally higher (SMD = + 0.96) on the posttest than those who ran CLASS.LD2 consultations. Statistical and educational significance were al so obtained across the experienced and inexperienced subjects when considered alone. An interaction, although not statistically significant (p \u3c .05), was obtained between group and experience level. Although there exist many similarities between the processes of building expert systems and concept analysis, incorporating both to develop an effective training tool had not previously been demonstrated. Results of this study indicated that the two fields, successfully combined, can create an effective and efficient training tool

Computer assisted instruction for students studying basic logic at the 9th grade level

A system of Computer Assisted Instruction for Students Studying Basic Logic at the 9th Grade Level was written in Prolog running on a Digital Equipment Vax 11/780 under the Unix1 operating system. The program is a straight-forward approach to teaching propositional logic. A three layer menu structure is used to inform students of lessons, subtopics, and help available. Example questions are generated after each subtopic, and student scores are displayed for immediate reinforcement or correction. Scores are also saved for storage on the student\u27s progress chart file. A help function enables the student to review explanations of terminolgy, review truth tables, print a student handbook, and either display or print the student\u27s progress chart. In addition, the teacher may open a student account, close a student account, print a classlist, print progress charts, or print the teacher\u27s handbook. The input/output is not up to par due to the limitations imposed by the Prolog C-Interpreter. Pretests and Posttests are only available interactively with the aid of the teacher

Artificial intelligence techniques for modeling database user behavior

The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model

An approach to the analysis and deisgn of an intelligent tutoring system using an object-oriented methodology

A true Intelligent Tutoring System is difficult to produce in today\u27s technological environment. This thesis reviews various theoretical methods and strategies that could be employed in performing the analysis and design of an Intelligent Tutoring System. An overview of the basic concepts of Object-Oriented Analysis and Design are provided in this thesis. The notation system provided by these concepts are utilized. The Object-Oriented Analysis and Design methods that are employed create a basis for an implementation of an Intelligent Tutoring System

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described

The relationship between computer interaction and individual user characteristics

Development of effective human computer interaction is being approached independently by two disciplines -- user interface design and computer aided instruction. The lack of communication between the two fields has left each separately pursuing different paths toward the same goals. This thesis attempts to bridge the gap between these two disciplines. An exploratory study was conducted to analyze whether user choices in a computer aided instruction environment and personality types as defined by the Myers-Briggs type indicator are related strongly enough to provide the basis for future user models. The results demonstrated that no single instructional strategy was preferred, implying the need for more than one user model. The amount of instruction chosen did not increase performance. These conclusions have impact on research efforts to understand how both user and system characteristics influence the use of computer technology. The current research efforts to incorporate artificial intelligence techniques by both user interface designers and computer aided instruction developers has heightened the need for knowledge-based systems incorporating interdisciplinary research efforts