Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...

Interactive Narrative and Intelligent Tutoring 14
dialogues which help in improving problem solving as well as declarative knowledge
acquisition by students [5, 6].
The nature of ill-defined domains, where there typically is a lack of clear
distinctions between “right” and “wrong” answers, makes the process of assessing
students’ progress and giving them appropriate feedback very difficult, if not
impossible. Despite of this obvious reality, ethics are taught at schools. If we look on
how ethical dilemmas are taught in face to face education, the teachers aim to lead the
student to the desired ethical choice by presenting facts and evidences that contradicts
his choice. By this way the student will be able to reason about his primary choice.
The mentioned technique shows that the teacher sets/identifies certain educational
goals and their target is to make the students reach these goals. At this point, the
teachers, from their points of view, have identified the right actions to be performed
within the presented dilemmas and try through discussion to convince the students
about these actions. Adopting this technique makes the use of a cognitive tutor
feasible in the ethical domain. AEINS is able to follow the Socratic Method in
teaching this type of dilemmas, similar to classroom education but within the context
of an evolved story.
In a domain like ethics, not all dilemmas can be treated in the same way. There are
many dilemmas where no right or wrong answer can be identified. For these
dilemmas we aim to discuss with the student the ideas behind the dilemma without
giving any specific answer at the end, the final judgment is up to the student.
Examples of such dilemmas are Kohlberg’s dilemmas [7]. These dilemmas have the
form of stories and can be easily incorporated in a narrative learning environment
with minor adaptations to fit in the whole system framework. This makes them highly
suited to serve as teaching moments in the educational IN.
In a way, the features of IN environments allow them to act as learning
environments for ill defined domains; they are able to provide the educational content
and facilitate the feedback process by incorporating it in the context of the story itself.
The idea of using a cognitive tutor in educational IN environments, i.e., a module that
selects problems for students in a personalized way, has not been sufficiently
investigated till now. We claim that including intelligent tutoring techniques in an IN
environment will:
1. Allow the student to act freely and take decisions that are incorporated, i.e., the
system acts adaptively to continue the ongoing narrative,
2. Monitor the student’s actions and follow the educational process, in addition to
giving appropriate feedback.
The cognitive tutor includes a domain model and student model that make use of
the model tracing and the knowledge tracing algorithms mentioned in [8]. The first
algorithm uses the model to interpret each student action and to follow the student’s
strategies as they work through problem scenarios. The results of model tracing are
used to provide the student with individualized teaching and feedback which is part of
the story context. The second algorithm, the knowledge tracing algorithm, is used to
estimate how well an individual student has mastered each concept. The results of
knowledge tracing are used to determine (a) the selection of problems relevant to
individual student needs and (b) when the student has mastered all the knowledge