How does one create a balanced AI curriculum in light of the popularity of Deep Learning?
I suppose the design of the curriculum revolves around the goal.

If the objective is to prepare students to enter the field as researchers, then the balance needs to favor the research that's active (and probably employable.) Where that fits into the AI picture as a whole is helpful. What limitations it has, and the missing pieces it doesn't solve are important to recognize, but have less weight than the targeted areas.

If the objective is to provide an overview, or survey, of the AI field, then deep learning and machine learning are only promising avenues to solve part of the puzzle. A balance in this case should include the history as well. Including the previous "promising avenues" that didn't deliver all they were supposed to deliver. Temper enthusiasm with reason and perspective.

Showing how the lesser-known areas are important, as well as their resistance to current approaches can help too. Even if the students will enter ML, a big-picture view will help them know where they're going.