A multi-dimensional taxonomy for automating hinting

Hints are an important ingredient of natural language tutorial dialogues. Existing models of hints, however, are limited in capturing their various underlying functions, since hints are typically treated as a unit directly associated with some problem solving script or discourse situation. Putting emphasis on making cognitive functions of hints explicit and allowing for automatic incorporation in a natural dialogue context, we present a multi-dimensional hint taxonomy where each dimension defines a decision point for the associated function. Hint categories are then conceived as convergent points of the dimensions. So far, we have elaborated four dimensions: (1) domain knowledge, (2) inferential role, (3) elicitation status, (4) problem referential perspective. These fine-grained distinctions support the constructive generation of hint specifications from modular knowledge sources.