Moral education refers to the cultivation of ideals, moral quality, culture, and discipline. One of its main tasks is to analyze students’ problem behaviors and identify their underlying need deficiencies. Previous psychological research has focused on studying how distinct factors affect psychological needs and problem behaviors. However, these findings have provided only scattered guidelines for identifying students’ need deficiencies, which are difficult for inexperienced teachers and parents to apply systematically. To address these issues, we attempt to answer two key research questions in this work. First, how do we define a theoretical framework so that the psychological research findings can be systematically applied to identify students’ need deficiencies? Second, can the latest AI technologies be employed to identify such need deficiencies automatically? To answer these research questions, we first build a theoretical framework to summarize all the factors relevant to the students’ problem behaviors and need deficiencies. After that, we propose and develop a task-oriented dialogue system that can properly inquire about different aspects of students’ information and automatically infer their need deficiencies. We conduct comprehensive experiments to evaluate the system’s performance with real-life cases. The results show that the built dialogue system could effectively serve as a diagnostic tool to identify the students’ need deficiencies.
A dialogue system for identifying need deficiencies in moral education