Technical Report: Giving Hints for Logic Programming Examples without Revealing Solutions
This work addresses a specific educational challenge in logic programming for students, but it is incremental as it builds on existing teaching methods without broader AI implications.
The authors tackled the problem of supporting learning in Answer Set Programming (ASP) by developing a framework that provides hints to students without revealing solutions, based on categorizing mistakes into syntactic, unexpected input, and semantic types with mathematical definitions.
We introduce a framework for supporting learning to program in the paradigm of Answer Set Programming (ASP), which is a declarative logic programming formalism. Based on the idea of teaching by asking the student to complete small example ASP programs, we introduce a three-stage method for giving hints to the student without revealing the correct solution of an example. We categorize mistakes into (i) syntactic mistakes, (ii) unexpected but syntactically correct input, and (iii) semantic mistakes, describe mathematical definitions of these mistakes, and show how to compute hints from these definitions.