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<Paper uid="W98-1430">
  <Title>SYSTEM DEMONSTRATION CONTENT PLANNING AS THE BASIS FOR AN * *INTELLIGENT*TUTORING SYSTEM</Title>
  <Section position="4" start_page="281" end_page="282" type="concl">
    <SectionTitle>
3. DIALOGUES GENERATED BYTHE SYSTEM
</SectionTitle>
    <Paragraph position="0"> At the top levels, the conversation generated by the system is hierarchical. Within each stage, the text is divided into segments, one for each *incorrect core variable. The variables are discussed in a partially ordered sequence which corresponds to the solution trace of the problem.</Paragraph>
    <Paragraph position="1"> Each variable is tutored using one of a number of tutoring methods which we have isolated from studies of human tutoring transcripts. The tutoring methods are implemented using an extended form of schema which allows full unification, static and dynamic preconditions, and recursion. The following schema is typical (schemata are implemented in Lisp): To correct student's ideas about any variable ?v controlled by the nervous system * Teach about mechanism of control of ?v Teach about when this mechanism is activated Check to find out whether student knows the correct answer now  Each tutoring method is composed of a number of topics. Unless it includes a recursive call to another schema, each topic is instantiated using standard text generation primitives like elicit and inform. In addition to arguments specifying the content, the primitives can be modified with arguments specifying where the primitive falls on Halliday's interpersonal and narrative axes. Thus, for example, a sentence like Remember that we're in the pre-neural period could be generated from a form like &lt;T-informs info=DR-info attitude=remind&gt;. Optional arguments are also provided for generating several kinds of discourse markers and temporal clauses.</Paragraph>
    <Paragraph position="2"> Instead of planning the complete text as in a monologue, we interleave planning and execution, planning only as much as necessary to generate the next turn. When the student gives an unexpected response, which includes various kinds of &amp;quot;near-misses&amp;quot; as well as wrong answers, we can choose between retrying the current goal, adding a new g0al at the top of the agenda, or dropping the current schema and replacing it by another one. In this way We can reply flexibly to the student while still maintaining a long- null And we're in the pre-neural period now / Remember that we're in the pre-neural period I So what do you think about TPR now? * Figur e 3: Sample dialogues range plan. Each path through Figure 3 shows one piece of conversation which can occur as a result of the schema shown above. From left to right, the paths show a right answer, a couple of near-misses which require the use of the more detailed knowledge base, and a wrong answer.</Paragraph>
  </Section>
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