Applications of AI in education

ion of the learning environment Many systems attempt to provide instruction by simulating a realistic working environment in which the student can learn the task. There are many reasons for developing such systems, including the possible danger of training using the actual equipment and the lack of domain experts who can devote their expensive time to training novices. Therefore, a realistic simulated learning environment can reduce both the cost and the risks of training. An example of a simulation-based ITS is the Advanced Cardiac Life Support (ACLS) Tutor [9] in which a student takes the role of team leader in providing emergency life support for patients who have had heart attacks. The system not only monitors student actions, but runs a realistic simulation of the patient's condition and maintains an environment that is reasonably faithful to the ``real life'' situation. Thus, the goal is not only to test the student's knowledge about the correct emergency procedures, but also to allow him to experience practicing those procedures in a more realistic manner than is possible in a traditional classroom. Some systems take a less rigorous approach to representing the environment; the situations presented are similar to the real world scenarios in which the knowledge could be applied, but they are not exact simulations. Smithtown [25] takes this approach by providing a simulated setting for students to test hypotheses about economics. However, the underlying model of the environment is not an exact simulation of how the laws of economics would be applied in the real world. Another example of such a system is the Design for Manufacturing Tutor [10]. At the extreme opposite of the simulation based tutors are those that teach knowledge in a decontextualized manner without attempting to simulate the real world. Many systems throughout the history of ITS research fall into this category [2, 24]. These systems provide problems for the learner to solve without trying to connect those problems to a real world situation and are designed to teach abstract knowledge that can be transferred to multiple problem solving situations. Emphasis of Instruction There is a long history of classifying instructional goals according to the type of knowledge being taught. An important early attempt at this classification is Bloom's taxonomy [4] and much recent work in categorizing knowledge has been derived from this. In addition to classifying learning goals by knowledge type, one can also examine what the student will be able to do upon completion of the ITS's lesson. This can vary from the student being able to perform a set of skills in a manner similar to an expert to understanding abstract concepts such as Newton's third law. For ease of development, systems tend to concentrate on teaching one type of knowledge. The most common type of ITS teaches procedural skills; the goal is for students to learn how to perform a particular task. There has been substantial research in cognitive psychology about human skill acquisition, so analyzing the domain knowledge in this framework can prove beneficial to instruction. Systems that are designed according to these principles are often called cognitive tutors. The most common result of this analysis is a set of rules that are part of a runnable expert model. This set of expert rules often serves double duty as a knowledge of the domain and as the pedagogical module. If a student encounters difficulty, the specific remediation required can be determined from the expert model. Page 3 of 11 Applications of AI in Education 5/3/2005 http://www.acm. org/crossroads/xrds3-1/aied.html An example of a ``cognitive tutor'' is SHERLOCK, which has tutorial actions associated with each state in the ``effective problem space'' [14]. Another example of an ITS that uses an analysis of expert behavior is the LISP tutor [3], which encodes expert problem solvers' actions as production rules, and attempts to determine which rules the student is having difficulty applying. Other ITSs concentrate on teaching concepts and ``mental models'' to students. These systems encounter two main difficulties. First, a more substantial domain knowledge is needed for instruction. Second, since learning concepts and frameworks is less well understood than learning procedures, there is less cognitive theory to guide knowledge representation and the pedagogical module. For these reasons, ITSs of this type require a larger domain knowledge base and are sometimes referred to as knowledge based tutors. As a result of not having a strong model of skill acquisition or expert performance, these systems are forced to use general teaching strategies. They also place more emphasis on the communication and presentation system in order to achieve learning gains. An example of such a system is the Pedagogical Explanation Generation (PEG) system [27] which has an explanation planning component that uses a substantial domain knowledge base to construct answers to student queries in the domain of electrical circuits. These classifications are really points along a continuum, and serve as good rules of thumb rather than a definitive method of classifying intelligent tutors. A system that does not fall into either of these categories is Coach [31], which teaches how to use UNIX mail. This is a procedural skill, and hence cognitive in nature. However, the emphasis of this system is also knowledge based and involves generating explanations and using general pedagogical tactics for generating feedback. Generally, tutors that teach procedural skills use a cognitive task analysis of expert behavior, while tutors that teach concepts and frameworks use a larger knowledge base and place more emphasis on communication to be effective during instruction. There are exceptions to these rules, but they serve as useful guidelines for classifying ITSs.