In this thesis, we present an artificial intelligence approach for tutoring students in
environments where there is a large repository of possible learning objects (eg texts,
videos). In particular, we advocate that students learn on the basis of past experiences of
peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring,
where a learning object's history-of-use is retained and leveraged to instruct future students.
We offer three distinct models that serve to deliver the required intelligent tutoring:(i) a�…

Our work takes as a starting point McCalla's proposed ecological approach for the design of
peer-based intelligent tutoring systems and proposes three distinct directions for research.
The first is to develop an algorithm for selecting appropriate content (learning objects) to
present to a student, based on previous learning experiences of like-minded students. The
second is to build on this research by also having students leaving explicit annotations on
learning objects to convey refinements of their understanding to subsequent students; the�…