Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. If a decision cannot be inferred from system rule base, a default rule is usually applied. In the paper a new interactive approach is proposed where in uncertain conditions an interactive inductive learning system can ask for human decision and improve its knowledge base with the rule derived from this decision. Problems and solutions of incorporation of human-made decision into rule base and aspects of choosing between static and incremental learning algorithms are analyzed in context of the proposed approach. An interactive inductive learning system is proposed to assist in study course comparative analysis.