Apprentice Learner Architecture
A computational model of learning from instruction and examples
The Apprentice Learner Architecture is a computational model of how people acquire cognitive skills from a mix of natural-language instruction and worked examples — much as a human tutor might teach a new procedure. Developed with Christopher MacLellan, Kenneth Koedinger, Daniel Weitekamp, and others, it grew out of earlier work on TRESTLE, a model of concept formation in structured domains. The architecture has been used to simulate students for testing intelligent tutoring systems, to study how the errors of simulated learners differ from those of human ones, and as the basis for “machine teaching” interfaces that let non-programmers train AI tutors by demonstration.
Work continues to build on the architecture, most recently in research on decomposed inductive procedure learning, which pushes it toward more human-like data efficiency.