Advanced training systems

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed

The desktop interface in intelligent tutoring systems

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine

An intelligent training system for payload-assist module deploys

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The Payload-Assist Module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system has, so far, proven to be a potentially valuable addition to the training tools available for training Flight Dynamics Officers in shuttle ground control. The authors are convinced that the basic structure of PD/ICAT can be extended to form a general architecture for intelligent training systems for training flight controllers and crew members in the performance of complex, mission-critical tasks

Designing intelligent computer‐based simulations: A pragmatic approach

This paper examines the design of intelligent multimedia simulations. A case study is presented which uses an approach based in part on intelligent tutoring system design to integrate formative assessment into the learning of clinical decision‐making skills for nursing students. The approach advocated uses a modular design with an integrated intelligent agent within a multimedia simulation. The application was created using an object‐orientated programming language for the multimedia interface (Delphi) and a logic‐based interpreted language (Prolog) to create an expert assessment system. Domain knowledge is also encoded in a Windows help file reducing some of the complexity of the expert system. This approach offers a method for simplifying the production of an intelligent simulation system. The problems developing intelligent tutoring systems are examined and an argument is made for a practical approach to developing intelligent multimedia simulation systems

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

An endorsement-based approach to student modeling for planner-controlled intelligent tutoring systems

An approach is described to student modeling for intelligent tutoring systems based on an explicit representation of the tutor's beliefs about the student and the arguments for and against those beliefs (called endorsements). A lexicographic comparison of arguments, sorted according to evidence reliability, provides a principled means of determining those beliefs that are considered true, false, or uncertain. Each of these beliefs is ultimately justified by underlying assessment data. The endorsement-based approach to student modeling is particularly appropriate for tutors controlled by instructional planners. These tutors place greater demands on a student model than opportunistic tutors. Numerical calculi approaches are less well-suited because it is difficult to correctly assign numbers for evidence reliability and rule plausibility. It may also be difficult to interpret final results and provide suitable combining functions. When numeric measures of uncertainty are used, arbitrary numeric thresholds are often required for planning decisions. Such an approach is inappropriate when robust context-sensitive planning decisions must be made. A TMS-based implementation of the endorsement-based approach to student modeling is presented, this approach is compared to alternatives, and a project history is provided describing the evolution of this approach

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

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