The goal of the Atlas project is to increase the opportunities for students to construct their own knowledge by conversing (in typed form) with a natural language-based ITS. Our previous research (Freedman et al., 2000; Freedman, 2000; Ros'e, 2000) has produced reusable components and tools for facilitating the development of domain specific tutorial dialogue systems. The domain independent Atlas system (Freedman et al., 2000) provides a general purpose planning engine and robust input understanding component that can be used to augment any tutoring system with dialogue capabilities. Natural language dialogue, which involves language understanding, planning, and language generation, offers a number of attractive features for intelligent tutoring systems. First, providing the opportunity for natural language input allows the system to assess student understanding based on the direct evidence of the content of student explanations rather than based on the indirect evidence of the problem solving mistakes the students make. Natural language dialogue provides a context in which the system can tailor its presentation of material more directly to the students' needs, for example by addressing student misconceptions immediately as they arise in conversation. Secondly, it gives students the opportunity to gain experience using the language of the domain they are learning. In contrast to using short menu interfaces, requiring students to type an answer triggers recall memory as opposed to recognition memory. Natural language dialogue makes it possible to build a more sophisticated type of tutor. In addition to the hints that many current tutors give, we can extend the tutor's repertoire to include the types of remediation subdialogues seen in natural human tutor...

content does not reflect the position or policy of the government and no official endorsement should be inferred. 1 Evens et al. / CIRCSIM-Tutor... CIRCSIM-Tutor uses natural language for both input and output. It can handle a variety of syntactic constructions and lexical items, including sentence fragments and misspelled words. It conducts a conversation with a student to help the student learn to solve a class of problems in cardiovascular physiology dealing with the regulation of blood pressure. We describe the lessons learned about knowledge representation, input understanding and text generation and how they influence the design of the next generation of our text-based ITS, CIRCSIM-Tutor Version 3, now under construction. 1

. The current generation of intelligent tutoring systems (ITS) have successfully produced learning gains without the use of natural language technology, but the goal for the next generation is to add natural language dialogue capabilities. Since it is already a tremendous effort to add domain and pedagogical knowledge to the current generation of ITSs, adding natural language dialogue capabilities can further increase the development time by requiring that language knowledge also be engineered. Rather than having natural language knowledge become an additional engineering burden, we seek to build tools that will allow us to attack the problem of pedagogical and language knowledge engineering in tandem. In this paper, we describe the authoring tool suite we are building to address this problem. We have found that our prototype tools do facilitate the rapid development of natural language dialogue interfaces for ITSs. With these tools we were able to build knowledge sources fo...

CIRCSIM-Tutor is a computer tutor designed to carry out a natural language dialogue with a medical student. Its domain is the baroreceptor reflex, the part of the cardiovascular system that is responsible for maintaining a constant blood pressure. CIRCSIM-Tutor’s interaction with students is modeled after the tutoring behavior of two experienced human tutors. The effectiveness of CIRCSIM-Tutor has been tested by 50 first-year medical students. Using a pretest/post-test paradigm we have demonstrated that significant learning occurs during a 1 hr interaction with the program. Students were also surveyed and indicated considerable satisfaction with the program.

The goal of the Atlas project is to increase the opportunities for students to construct their own knowledge by conversing (in typed form) with a natural language-based ITS. In this paper we describe two components of Atlas|APE, the integrated planning and execution system at the heart of Atlas, and CARMEL, the natural language understanding component. These components have been designed as domainindependent rule-based software, with the goal of making them both extensible and reusable. We illustrate the use of CARMEL and APE by describing Atlas-Andes, a prototype ITS built with Atlas using the Andes physics tutor as the host.

. Language-based Socratic tutoring systems face the problem of handling unconstrained student input. By asking closed, short-answer questions, the CIRCSIMTutor dialogue-based intelligent tutoring system has rendered the free-text input understanding task more tractable. Even though the tutor's questions can be answered very simply, CIRCSIM-Tutor has preserved quite a number of the pedagogical advantages and opportunities of unconstrained student input. By using only simple language processing facilities the tutor is able to respond appropriately to a high percentage of student turns. 1

We describe a framework that helps students learn from examples by generating example problem solutions whose level of detail is tailored to the students' domain knowledge. The framework uses natural language generation techniques and a probabilistic student model to selectively introduce gaps in the example solution, so that the student can practice applying rules learned from previous examples in problem solving episodes of difficulty adequate to her knowledge. Filling in solution gaps is part of the meta-cognitive skill known as selfexplanation (generate explanations to oneself to clarify an example solution), which is crucial to effectively learn from examples. In this paper, we describe how examples with tailored solution gaps are generated and how they are used to support students in learning through gap-filling self-explanation. 1

Abstract. Scalability and reusability of tutorial dialogue systems is a function of the corresponding characteristics in their component tutoring and dialogue technologies. This paper discusses an architecture for a scalable, reusable spoken conversational tutor, SCoT. With this design we hope to minimize the efforts needed to reuse the

Abstract: We describe a dialogue system construction tool that supports the rapid development of dialogue systems for learning applications. Our goals in developing this tool were to provide 1) a plug-and-play type of system that facilitates the integration of new modules and experimentation with different core modules 2) configuration options that effect the behavior of the modules so that the system can be flexibly fine-tuned for a number of learning studies and 3) an authoring language for setting up the domain knowledge and resources needed by the system modules.

Abstract. In this contribution we present the Dialogue-based Reflective