  Along with the development of systems for natural language understanding and
generation, dialog systems have been widely adopted for language learning and
practicing. Many current educational dialog systems perform chitchat, where the
generated content and vocabulary are not constrained. However, for learners in
a school setting, practice through dialog is more effective if it aligns with
students' curriculum and focuses on textbook vocabulary. Therefore, we adapt
lexically constrained decoding to a dialog system, which urges the dialog
system to include curriculum-aligned words and phrases in its generated
utterances. We adopt a generative dialog system, BlenderBot3, as our backbone
model and evaluate our curriculum-based dialog system with middle school
students learning English as their second language. The constrained words and
phrases are derived from their textbooks, suggested by their English teachers.
The evaluation result demonstrates that the dialog system with curriculum
infusion improves students' understanding of target words and increases their
interest in practicing English.
